100+ Best Free Machine Learning Courses

Best Free Machine Learning Courses and Deep Learning Courses

In this post, we'll go through 100+ free machine learning courses created and taught by world's best universities, (like Stanford University, MIT University, Harvard University, etc) tech giants, (like Google, Microsoft, IBM, Intel, etc) leading organizations and platforms, (like Kaggle, DeepMind, DeepLearning AI, Fast AI, etc) and one of the best professors and experts (like Andrew Ng, Laurence Moroney, Christopher Manning, Sebastian Thrun, Rachel Thomas, Fei-Fei Li, Jeremy Howard, etc). This best free machine learning courses are suitable for beginners, intermediate and advanced machine learning practitioners.

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Starting with...

Introduction to Machine Learning

Best Free Machine Learning Course for Beginners

πŸ‘‰ Machine Learning Course for Beginners

Created by: FreeCodeCamp

About this Course:

Few months ago, FreeCodeCamp has released a 10-hour machine learning course for beginners on their YouTube channel. In this course, You'll learn about 

1. Basics of Machine Learning

2. Linear Regression & Regularization

3. Logistic Regression & Performance Metrics

4. Support Vector Machine

5. Principal Component Analysis

6. Learning Theory

7. Decision Trees & Random Forest

8. Learning more algorithms and building more projects

9. Unsupervised Learning Algorithms

πŸ‘‰ Intro to Machine Learning

Created by: Kaggle

About this Course:

Learn the core ideas in machine learning, and build your first models. This course is for those who have earlier dipped their hands in Python. The topics covered in this course will be... 

1. How Models Work - The first step if you're new to machine learning

2. Basic Data Exploration - Load and understand your data

3. Your First Machine Learning Model - Building your first model. Hurray!

4. Model Validation - Measure the performance of your model ? so you can test and compare alternatives

5. Underfitting and Overfitting - Fine-tune your model for better performance.

6. Random Forests - Using a more sophisticated machine learning algorithm.

7. Machine Learning Competitions - Enter the world of machine learning competitions to keep improving and see your progress

πŸ‘‰ Machine Learning Course by Stanford University 

Taught by: Andrew Ng

About this Course:

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Review of User Who had Completed this Course:

Very nice course. It gives a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.

- This course is amazing and covers most of the ML algorithms. I really liked that this course has emphasized math behind each technique which helps to choose the best algorithm while solving a problem.

πŸ‘‰ Machine Learning Foundation

Taught by: Laurence Moroney (AI Lead at Google)

About this Course:

Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow with Laurence Moroney. 

πŸ‘‰ Introduction to Machine Learning by Carnegie Mellon University

Taught by: Matt Gormley (Director of Machine Learning at CML)

About this Course:

By the end of the course, students should be able to:

- Implement and analyze existing learning algorithms, including well-studied methods for classification, regression, structured prediction, clustering, and representation learning

- Integrate multiple facets of practical machine learning in a single system: data preprocessing, learning, regularization and model selection

- Describe the formal properties of models and algorithms for learning and explain the practical implications of those results

- Compare and contrast different paradigms for learning (supervised, unsupervised, etc.)

- Design experiments to evaluate and compare different machine learning techniques on real-world problems

- Employ probability, statistics, calculus, linear algebra, and optimization in order to develop new predictive models or learning methods

πŸ‘‰ Introduction to Machine Learning Course 

Taught by: Sebastian Thrun (Co-founder of Udacity)

About this Course:

In this course, you’ll learn by doing! We’ll bring machine learning to life by showing you fascinating use cases and tackling interesting real-world problems like self-driving cars. For your final project you’ll mine the email inboxes and financial data of Enron to identify persons of interest in one of the greatest corporate fraud cases in American history.

When you finish this introductory course, you’ll be able to analyze data using machine learning techniques, and you’ll also be prepared to take our Data Analyst Nanodegree. We’ll get you started on your machine learning journey by teaching you how to use helpful tools, such as pre-written algorithms and libraries, to answer interesting questions.

Review of User Who had Completed this Course:

This is practical course, instructors are nice. If you like python you would love this course. Mathematics is not strong here but this an Intro to Machine learning and they are doing the best they can to expose us not only to machine learning algorithm but sci-kit learn api which keeps you hooked on this course. Once you get the idea of any algorithm you can go deeper into mathematical aspects of it. One of the issue I faced was the problem with quizzes few often they are a little opaque.

πŸ‘‰ A Gentle Introduction to Machine Learning

Taught by: Josh Tarmer

About this Course:

This course covers a lot of topics and this can be intimidating. However, there is no reason to fear, this play list will help you trough it all, one step at a time.

πŸ‘‰ Machine Learning Fundamentals by UC San Diego

Taught by: Sanjoy Dasgupta

About this Course:

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Review of User Who had Completed this Course:

The course provides the "fundamentals" in only first few chapters. The more the courses advances the less mathematics is presented which hampers understanding of the subject. The quiz and homework problems have unlimited (!) number of attempts and provide no (!) explanations after the correct answer is given. This has been explained by the staff on the forum as a result of lack of time for preparing the course. If the missing explanations were provided, I would be giving the course 4 stars, and 5 stars if optional, more advanced mathematics videos were present.

πŸ‘‰ Introduction to Machine Learning

Taught by: Anton Boitsev, Aleksei Romanov, Dmitry Volchek, Elena Mikhailova, Natalia Grafeeva and Olga Egorova

About this Course: 

This course is an introduction to machine learning. It will cover the modern methods of statistics and machine learning as well as mathematical prerequisites for them. We will discuss the methods used in classification and clustering problems. You will learn different regression methods.

Various examples and different software applications are considered in the course. You will get not only the theoretical prerequisites, but also practical hints on how to work with your data in MS Azure.

πŸ‘‰ Machine Learning from Data

Taught by: Malik Magdon-Ismail (Computer Science Professor at Caltech)

About this Course: 

Online Edition of Machine Learning From Data taught by Professor Malik Magdon-Ismail at Rensselaer in Fall 2020. Covers the fundamental questions of learning as well as some advanced techniques.

πŸ‘‰ Machine Learning Explanability

Taught by: Dan Becker (Data Scientist)

About this Course: 

Extract human-understandable insights from any machine learning model. Topics covered in this course are

1. Use Cases for Model Insights - Why and when do you need insights?

2. Permutation Importance - What features does your model think are important?

3. Partial Plots - How does each feature affect your predictions?

4. SHAP Values - Understand individual predictions

5. Advanced Uses of SHAP Values - Aggregate SHAP values for even more detailed model insights

πŸ‘‰ Introduction to Machine Learning for Coders!

Taught by: Jeremy Howard

About this Course: 

Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.

Review of User Who had Completed this Course:

Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms. But Jeremy and Rachel (Course Professors) believe in the theory of 'Simple is Powerful', by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the 'magic' Deep Learning.

πŸ‘‰ Theoretical Machine Learning Lecture Series

Taught by: Christopher Manning (Director at Stanford AI Lab)

About this Course: 

In this public lecture, Manning will discuss some of the results in computer vision, speech, and language which support the preceding claims. Manning will also explore bigger questions including why and how deep learning methods manage to be so successful, what new perspectives they suggest about human cognition and the language of thought, and what opportunities exist for deep learning to move beyond its core successes on sensory perception and classification tasks to be a broader tool for artificial intelligence.

πŸ‘‰ 50 Must Know Concepts, Algorithms in Machine Learning

Created by: The Machine Learning 

About this Course:

This course is designed to give you introduction to syllabus of machine learning. If you want to get started with machine learning then this course will help you. It helps you to get ready for an interview with 50 concepts covering varied range of topics. The course is intended not only for candidates with a full understanding of Machine Learning but also for recalling knowledge in data science.

Reviews of Users Who have Taken this Course:

This won't teach you how to do or use machine learning but it will give you a very good, brief introduction to the various techniques, methods employed in ML / AI and some of the Python modules used in AI. A very good introductory course to AI. I've no experience of any of these methods since I'm just starting out in AI, but feel I have a basic idea now of some of the methods the could be employed. I'm impressed by the depth of knowledge, shown by the instructor.

πŸ‘‰ CSEP 546 - Machine Learning

Taught by: Geoff Hulton

About this Course: 

The topics covered in this course are

- Introduction

- Overview of Machine Learning

- Basics of Evaluating Models

- Logistic Regression

- Overview of Machine Learning

- Basics of Evaluating Models

- Logistic Regression

- Feature Engineering (Text)

- ROC Curves and Operating Points

- Feature Engineering (Text)

- ROC Curves and Operating Points

- Extra Content: Feature Engineering & Bag of Words

- Bounds and Comparing Models

- Naive Bayes

- Implementing with Machine Learning

- Bounds and Comparing Models

- Naive Bayes

- Implementing with Machine Learning

- Extra Content: Approaching the Kaggle Assignment

- Decision Trees

- Defining Success with ML Systems

- Intelligent User Experiences

- Decision Trees

- Defining Success with ML Systems

- Intelligent User Experiences

- Overfitting and Underfitting

- Design Pattern - Adversarial Learning

πŸ‘‰ Introduction to Machine Learning

Taught by: Jonathan Shewchuk

About this Course:

This class introduces algorithms for learning, which constitute an important part of artificial intelligence.

Topics include

- Classification: perceptrons, support vector machines (SVMs), Gaussian discriminant analysis (including linear discriminant analysis, LDA, and quadratic discriminant analysis, QDA), logistic regression, decision trees, neural networks, convolutional neural networks, boosting, nearest neighbor search;

- Regression: least-squares linear regression, logistic regression, polynomial regression, ridge regression, Lasso;

- Density estimation: maximum likelihood estimation (MLE);

- Dimensionality reduction: principal components analysis (PCA), random projection; and
clustering: k-means clustering, hierarchical clustering, spectral graph clustering.

πŸ‘‰ Foundations of machine learning and statistical inference

Taught by: Anima Anandkumar and Sahin Lale

About this Course:

The course assumes students are comfortable with analysis, probability, statistics, and basic programming. This course will cover core concepts in machine learning and statistical inference. The ML concepts covered are spectral methods (matrices and tensors), non-convex optimization, probabilistic models, neural networks, representation theory, and generalization. In statistical inference, the topics covered are detection and estimation, sufficient statistics, Cramer-Rao bounds, Rao-Blackwell theory, variational inference, and multiple testing. In addition to covering the core concepts, the course encourages students to ask critical questions such as: How relevant is theory in the age of deep learning? What are the outstanding open problems? Assignments will include exploring failure modes of popular algorithms, in addition to traditional problem-solving type questions.

πŸ‘‰ Machine Learning Fundamentals

Taught by: Varun Chandola

About this Course: 

You will learn the fundamental concepts necessary to understand any Machine Learning algorithm. At the same time, we will also go deep into some key ML algorithms, but the objective would be to understand the foundational aspects of ML through these algorithms, and not learning how to use the algorithm itself. We will be looking at both supervised and unsupervised learning settings.

πŸ‘‰ Applied Machine Learning

Taught by: Andreas C. MΓΌller

About this Course:

This class offers a hands-on approach to machine learning and data science. The class discusses the application of machine learning methods like SVMs, Random Forests, Gradient Boosting and neural networks on real world dataset, including data preparation, model selection and evaluation. Apart from applying models, we will also discuss software development tools and practices relevant to productionizing machine learning models.

πŸ‘‰ Introduction to Machine Learning

Taught by: Balaraman Ravindran (IIT Madras Professor)

About this Course: 

In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

Review of User Who had Completed this Course:

Great course for beginners to step into the world of ML :) Excellent. Also try other AI courses from NPTEL. They are awesome.

πŸ‘‰ CS229: Machine Learning 

Taught by: Andrew Ng and Other Experts

Review of User Who had Completed this Course:

This was a very well-designed class. Each problem set was lovingly crafted, and each problem helped me understand the material (there weren't any "filler" problems or long derivations where I learned nothing). The lectures were fantastic, and if you didn't like watching lectures, the lecture notes were great too.

The class was a lot of work. Each set took one or two days, but there were only four sets in the class. There was a tightly timed midterm and an open-ended project with four deliverables (a proposal, a milestone, a poster presentation, and a final report).

The problem sets were a mixture of math and programming. The programming part was not hard if you know MATLAB, and the questions were constructed so that you'd know when you got them right. The math part was somewhat harder, but if you had a good background in calculus, linear algebra, and statistics, you'd be fine. (They did have extra lectures on MATLAB, linear algebra, and statistics, so you'd probably be fine either way.)

πŸ‘‰ Fundamentals of Machine Learning

Taught by: Dr. Artemy Kolchinsky and Dr. Brendan Tracey

About this Course: 

In this small course, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally, it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.

Review of User Who had Completed this Course:

Very well structured with the seal of the Santa Fe Institute. It's a good qualitative and intuitive introduction to anyone interested on machine learning in just some classes. I recommend it.

πŸ‘‰ FA18: Machine Learning

Taught by: Charles Isbell (Professor at The Georgia Institute of Technology)

About this Course: 

In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

- Statistical supervised and unsupervised learning methods

- Randomized search algorithms

- Bayesian learning methods

- Reinforcement learning

πŸ‘‰ Introduction to Machine Learning

Taught by: Tom Mitchell and Maria-Florina Balcan

About this Course:

This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

πŸ‘‰ Machine Learning by University of Texas

Taught by: Adam Klivans and Qiang Liu

About this Course:

In this course, the topics that will be covered are pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.

πŸ‘‰ Learning from Data (Introductory Machine Learning course) by California Institute of Technology

Taught by: Yaser Abu-Mostafa (Computer Science Professor at CalTech)

About this Course:

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:

Review of User Who had Completed this Course:

The best online machine learning course I've taken (I've completed courses by Andrew Ng as well as Hastie and Tibshirani et al), this course covers rigorous theory as well as practical aspects, setting you up for a very solid foundation for future study in machine learning. Assignments are challenging and really require you to understand and engage with the material. Prof Abu-Mostafa's teaching quality is amazing and even highly complex concepts are clearly presented.

πŸ‘‰ Introduction to Machine Learning (IIT KGP)

Taught by: Sudeshna Sarkar (Professor at IIT Kharagpur)

About this Course: 

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbor, an introduction to Bayesian learning and the naΓ―ve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms.

πŸ‘‰ In-depth introduction to machine learning in 15 hours of expert videos

Taught by: Trevor Hastie and Robert Tibshirani

About this Course:

List of chapters covered in this Machine Learning Course

Chapter 1: Introduction

Chapter 2: Statistical Learning

Chapter 3: Linear Regression

Chapter 4: Classification

Chapter 5: Resampling Methods 

Chapter 6: Linear Model Selection and Regularization

Chapter 7: Moving Beyond Linearity

Chapter 8: Tree-Based Methods

Chapter 9: Support Vector Machines

Chapter 10: Unsupervised Learning

Last: Interviews with John Chambers, Bradley Efron, Jerome Friedman and Statistics Graduate Students

πŸ‘‰ Real World Machine Learning Case Study

Created by: Pianalytix 

About this Course: 

Machine learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:

- Create a common ground of understanding, this will ensure the right mindset

- State early how progress should be measured

- Communicate clearly how different machine learning concepts works

- Acknowledge and consider the inherited uncertainty, it is part of the process

πŸ‘‰ Machine Learning

Taught by: Carl Gustaf Jansson (AI Professor at KTH Royal Institute of Technology)

About this Course: 

The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree based inductive learning, inductive logic programming, reinforcement learning and deep learning through decision trees.

πŸ‘‰ Machine Learning

Taught by: John W. Paisley (Professor at Columbia University)

About this Course: 

In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.

In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Review of User Who had Completed this Course:

There are not many courses online that provide such in-depth learning experience in Machine Learning. This course goes into some details and mathematics of the algorithms being used. It demands a good amount of time every week to understand and apply all that is being taught but that is what makes it good. It is not like many other courses that you can take and pass with minimal effort but at the end of it, it is worth spending time taking this course.

πŸ‘‰ Foundations of Machine Learning

Taught by: David S. Rosenberg (CTO and Data Scientist at Bloomberg) 

About this Course: 

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning.

Review of User Who had Completed this Course:

One of the best courses on the foundations of machine learning that I've encountered anywhere. This course repeatedly goes beyond the simplistic and occasionally misleading explanations provided by other high-profile introductory courses. For example, the discussion of the geometric interpretation of regularisation in terms of isocontours in parameter space goes a level deeper than the equivalent account offered in other courses, providing real insight and intuition. That's typical of this course. The lecturer does a great job of elucidating the subtleties of the subject, assuming very little prior knowledge.

πŸ‘‰ Machine Learning

Taught by: Michael Littman and Charles Isbell

About this Course: 

This is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.

In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!

Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!

Reviews of Users Who had Completed this Course:

- An excellent overview of the field. The lectors are great, and I particularly liked the cross-references and similarities between different topics that they show.

- Superb course. At every step they probe how we should choose what to do next instead of just telling the steps.

πŸ‘‰ Introduction to Machine Learning and Pattern Recognition

Taught by: Aleix M. Martinez

About this Course:

This course covers the topics of machine learning and statistical pattern recognition. This is a 14-week (1 semester) course.  The videos are to be watched in succession since each lecture draws on previous lectures.

Each video in this series corresponds to a class lecture. You will find the slides associated with each lecture in the description of each video. You may also see links to scientific articles used in that lecture and maybe links to fun videos.

πŸ‘‰ Microsoft AI Classroom

Taught by: Rohini Srivathsa, Amit Aggarwal, Sangeeta Gupta, and Other Experts

About this Course:

The three (3) hour session will cover key topics to introduce students to AI, Machine learning and Data Sciences topics with lab exercises. Each module will be introduced by an industry leader and subject matter expert

πŸ‘‰ Artificial Intelligence

Taught by: Ansaf Salleb-Aouissi (Department of Computer Science, Columbia University)

About this Course:

Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Reviews of Users Who have Taken this Course:

This is a great course. I would highly recommend this course to anyone wanting an introduction to AI. The course itself was quite challenging and definitely worth the verified certificate. Although the quizzes were just a bit tough, the programming assignments were extremely hard (in a good way). The projects were fun and interesting. Do however note that they might suck up a lot of your time (20-25 hrs on average)

Also, this course has quite a bit of math in it. I took this course when I didn't know much of linear algebra and calculus and in some parts of the course, my mind was totally blank. Now , after finishing the course and after learning some of that math, I just went through those videos which had previously left me totally blank and I was able to easily comprehend the material. I also managed to pass quite easily even without the math ( I even left out a programming assignment and still got a 82%) so if you don't have the math knowledge, don't worry.

Coming to the involvement of the staff and TAs, it wasn't great. At the start of this course (around week 1-4), the staff was extremely responsive and helped out tons of students. However, as the course progressed, involvement went down. Technical problems would go uncorrected for days and questions would be left unanswered for weeks(Note that quiz errors were still getting corrected throughout the course if those errors were reported quickly). Even with those problems, the course and its contents, engaged me. Fortunately, the staff realized the problem and became active in the last few weeks, with even the course lecturer, who is truly an amazing professor :), helping us out.

πŸ‘‰ Welcome to Artificial Intelligence!

Taught by: Vinoth Rathinam

About this Course: 

NON TECHNICAL COURSE specifically created for AI/ML/DL Aspirants, gives insight about Road map to A.I

This course will clear all doubts such as,

1. What are prerequisites for learning AI?

2. What is Road map to start Machine learning project(ML)

3. How to choose the best programming language for AI ?

4. How much Mathematical knowledge needed for AI ?

5. Which is the best AI Engine/Tool/Framework for AI ? and so on..

Review of User Who had Completed this Course:

Probably the best free course on udemy to get complete insight into Artificial Intelligence. The tutor is awesome and explained the concepts like a piece of cake. If you want to know about Artificial Intelligence and Machine Learning briefly, you are the right course

πŸ‘‰ Introduction to Artificial Intelligence

Taught by: Pieter Abbeel and Daniel Klein

About this Course: 

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

πŸ‘‰ Demystifying AI/ML/DL

Created by: AWS and Amazon

About this Course: 

After taking this set of courses, you’ll understand how artificial intelligence (AI) led to machine learning (ML), which then led to deep learning (DL).

πŸ‘‰ CS221 Artificial Intelligence Course

Taught by: Moses Charikar and Dorsa Sadigh

About this Course: 

In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Machine Learning with Python

Machine Learning with Python Course

πŸ‘‰ Machine learning in Python with scikit-learn

Taught by: Olivier Grisel, Loic Esteve, Guillaume Lemaitre & Others

About this Course:

This course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science.

The course is more than a cookbook: it will teach you to be critical about each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions.

The training will be essentially practical, focusing on examples of applications with code executed by the participants.

πŸ‘‰ Machine Learning with Python

Taught by: Harrison Kinsley

About this Course: 

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.

πŸ‘‰ Machine Learning with Python: A Practical Introduction

Created by: IBM

About this Course: 

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

Review of User Who had Completed this Course:

This course is introducing the basics of ML but never touch on higher levels like cross-validation, newer ML algorithms etc. More for beginners.

πŸ‘‰ Machine Learning with Python

Taught by: Daniel Tran, Kevin Wong, Saeed Aghabozorgi

About this Course: 

This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

πŸ‘‰ Machine Learning with Python

Taught by: Dhaval Patel

About this Course: 

Machine learning tutorial playlist that is best suitable for a total beginner. It will start with basics of machine learning, cover various ML algorithms for regression and classification, feature engineering and also includes some real life end to end projects. In terms of technology the author has used these tools: sklearn,python,pandas,numpy,jupyter notebook, excel, tensorflow

πŸ‘‰ Machine Learning by Intel

Created by: Intel

About this Course: 

By the end of this course, students will have practical knowledge of:

- Supervised learning algorithms

- Key concepts like under- and over-fitting, regularization, and cross-validation

- How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model

πŸ‘‰ Machine Learning with Python - From Linear Models to Deep Learning

Taught by: Regina Barzilay and Tommi Jaakkola

About this Course: 

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

- Representation, over-fitting, regularization, generalization, VC dimension;

- Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;

- On-line algorithms, support vector machines, and neural networks/deep learning

Review of User Who had Completed this Course:

This is an excellent course, well taught by both professors, with challenging problem sets and exams, and with interesting programming assignments in python. There are prerequisites in terms of probability, statistics, linear algebra and python coding, but this is intended to be taken as the final course in the micromasters so much is covered in previous courses, and the prerequisites are clearly stated. I enjoyed and learnt a lot from this course (it’s not a watered down, easy version but that it what makes it rewarding and worthwhile). If you have the right background and prerequisites, do not be put off by the previous reviewers!

Mathematics for Machine Learning

Mathematics for Machine Learning Courses

πŸ‘‰ Mathematics of Machine Learning

Taught by: Philippe Rigollet

About this Course: 

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.

πŸ‘‰ Mathematics of Machine Learning by Microsoft Research

Taught by: Robert Schapire

About this Course: 

The purpose of this course is to introduce graduate students (and advanced undergraduates) to these foundational results, as well as to expose them to the new and exciting modern challenges that arise in deep learning and reinforcement learning. 

πŸ‘‰ Linear Algebra by MIT

Taught by: Gilbert Strang

About this Course: 

This course parallels the combination of theory and applications in Professor Strang’s textbook Introduction to Linear Algebra. The course picks out four key applications in the book: Graphs and Networks; Systems of Differential Equations; Least Squares and Projections; and Fourier Series and the Fast Fourier Transform.

πŸ‘‰ Mathematics for Machine Learning: Multivariate Calculus

Taught by: Samuel J. Cooper, David Dye and A. Freddie Page

About this Course: 

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

Review of User Who had Completed this Course:

The course is a great introduction to how one can translate pre-learned mathematical concepts into machine learning. I think it just makes you appreciate complicated mathematical equations as they are tied into neat computational applications.
For those who want an introduction to the math first, the course has plenty of explanatory videos as well. But as someone who did know the math, it just made me realize that my college math can actually be used to do something useful.

πŸ‘‰ Probability and Statistics

About this Course: 

Topics covered in this course are

UNIT 1: Introduction

UNIT 2: Exploratory Data Analysis

UNIT 3: Producing Data

UNIT 4: Probability

UNIT 5: Inference

πŸ‘‰ Probabilistic Machine Learning 

Taught by: Philipp Hennig

About this Course: 

This playlist collects the lectures on Probabilistic Machine Learning by Philipp Hennig at the University of TΓΌbingen during the Summer Term of 2020.

The course covers the probabilistic ("Bayesian") paradigm for machine learning, and occasionally draws direct connections to statistical (e.g. Lecture 10) and deep learning (e.g. Lecture 8). The course is aimed at master students in computer science and related fields.

πŸ‘‰ Essential Mathematics for Machine Learning

Taught by: Prof. Sanjeev Kumar and Prof. S. K. Gupta

About this Course: 

This particular topic is having applications in all the areas of engineering and sciences. Various tools of machine learning are having a rich mathematical theory. Therefore, in order to develop new algorithms of machine/deep learning, it is necessary to have knowledge of all such mathematical concepts. In this course, we will introduce these basic mathematical concepts related to the machine/deep learning. In particular, we will focus on topics from matrix algebra, calculus, optimization, and probability theory those are having strong linkage with machine learning. Applications of these topics will be introduced in ML with help of some real-life examples

πŸ‘‰ MIT RES.LL-005 Mathematics of Big Data and Machine Learning

Taught by: Jeremy Kepner and Vijay Gadepally

About this Course: 

This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data. This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software. The class will begin with a number of practical problems, introduce the appropriate theory, and then apply the theory to these problems. Students will apply these ideas in the final project of their choosing. The course will contain a number of smaller assignments which will prepare the students with appropriate software infrastructure for completing their final projects.

πŸ‘‰ Computational Linear Algebra for Coders

Taught by: Rachel Thomas

About this Course: 

This course was taught in the University of San Francisco's Masters of Science in Analytics program, summer (for graduate students studying to become data scientists). The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons.

πŸ‘‰ Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Taught by: Gilbert Strang

About this Course: 

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

Neural Networks for Machine Learning

Neural Networks for Machine Learning

πŸ‘‰ Neural Networks for Machine Learning

Taught by: Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky

About this Course: 

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

πŸ‘‰ A Small Playlist on Neural Networks

Created by: 3Blue1Brown

About this Course: 

Topics covered in this course are

- What is Neural Network?

- Gradient Descent, How Neural Networks Learn?

- What is Backpropagation really doing?

- Backpropagation Calculus

πŸ‘‰ CS231N: Convolutional Neural Networks for Visual Recognition by Stanford

Taught by: Fei-Fei Li, Serena Yeung, and Justin John

About this Course: 

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

πŸ‘‰ IBM Introduction to Deep Learning & Neural Networks with Keras

Taught by: Alex Aklson

About this Course: 

After completing this course, learners will be able to:

• describe what a neural network is, what a deep learning model is, and the difference between them.

• demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.

• demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • build deep learning models and networks using the Keras library.

πŸ‘‰ CMU Neural Nets for NLP 2020

Taught by: Graham Neubig

About this Course: 

This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Spring 2020) covers:

- Introduction to Neural Networks

- Class Outline

- Example Tasks and Their Difficulties

- What Neural Nets can Do To Help

πŸ‘‰ Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Taught by: Andrew Ng

About this Course: 

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.

Reviews of Users Who had Completed this Course:

- I finished this second deep learning course and I like it very much. I am looking forward for more courses in this deep learning series. Andrew is doing a great job here.

- This is a follow up course to Neural Networks and Deep Learning so you must start with the latter. The practical side of the teaching was very interesting.

πŸ‘‰ Neural Network Programming - Deep Learning with PyTorch

Created by: Deep Lizard

About this Course: 

This series is all about neural network programming and PyTorch! We'll start out with the basics of PyTorch and CUDA and understand why neural networks use GPUs. We then move on to cover the tensor fundamentals needed for understanding deep learning before we dive into neural network architecture. From there, we'll go through the details of training a network, analyzing results, tuning hyperparameters, and using TensorBoard with PyTorch for visual analytics!

πŸ‘‰ Fuzzy Logic and Neural Networks

Taught by: Dilip Kumar Pratihar

About this Course: 

This course will start with a brief introduction to fuzzy sets. The differences between fuzzy sets and crisp sets will be identified. Various terms used in the fuzzy sets and the grammar of fuzzy sets will be discussed, in detail, with the help of some numerical examples. The working principles of two most popular applications of fuzzy sets, namely fuzzy reasoning and fuzzy clustering will be explained, and numerical examples will be solved. 

Deep Learning

Best Deep Learning Courses

πŸ‘‰ Intro to Deep Learning

Created by: Kaggle

About this Course: 

Use TensorFlow and Keras to build and train neural networks for structured data. The topics covered in this course are

1. A Single Neuron - Learn about linear units, the building blocks of deep learning.

2. Deep Neural Networks - Add hidden layers to your network to uncover complex relationships.

3. Stochastic Gradient Descent - Use Keras and Tensorflow to train your first neural network.

4. Overfitting and Underfitting - Improve performance with extra capacity or early stopping.

5. Dropout and Batch Normalization - Add these special layers to prevent overfitting and stabilize training.

6. Binary Classification - Apply deep learning to another common task.

- Bonus Lesson

Detecting the Higgs Boson With TPUs - Get started with Tensor Processing Units (TPUs)!

πŸ‘‰ Intro to Deep Learning

Created by: MIT

About this Course: 

MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!

πŸ‘‰ Practical Deep Learning for Coders (2020)

Taught by: Jeremy Howard

About this Course: 

Each video covers a chapter from the book. The entirety of every chapter of the book is available as an interactive Jupyter Notebook. Jupyter Notebook is the most popular tool for doing data science in Python, for good reason. It is powerful, flexible, and easy to use. We think you will love it! Since the most important thing for learning deep learning is writing code and experimenting, it's important that you have a great platform for experimenting with code.

πŸ‘‰ Intro to TensorFlow for Deep Learning

Created by: Tensorflow

About this Course: 

Learn how to build deep learning applications with TensorFlow. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.

Review of User Who had Completed this Course:

This course will be very useful for beginners who would like to know about each and every basic concepts in deep learning. The examples they teach are wonderful.

Full Stack Deep Learning

Created by: Full Stack Deep Learning

About this Course: 

In this course, we teach the full stack of production Deep Learning:

- Formulating the problem and estimating project cost

- Finding, cleaning, labeling, and augmenting data 

- Picking the right framework and compute infrastructure

- Troubleshooting training and ensuring reproducibility

- Deploying the model at scale

πŸ‘‰ Deep Learning With Tensorflow 2.0, Keras and Python

Taught by: Dhaval Patel

About this Course: 

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python

πŸ‘‰ Deep Learning (with PyTorch)

Taught by: Yann LeCun

About this Course: 

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

πŸ‘‰ Deep Learning for NLP at Oxford with Deep Mind

Taught by: Phil Blunsom and Other Experts

About this Course: 

This is an applied course focusing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.

πŸ‘‰ Deep Learning Architectures

Taught by: Yannic Kilcher

About this Course: 

The topics covered by authors are 

- Neural Ordinary Differential Equation

- A Statistical Test for detecting Adversarial Examples

- Batch Normalization - Accelerating Deep Network Training

- Stochastic RNNs

- Challenging common assumption in supervised learning

- Manifold Mixup

- Dynamic Routing

- SinGAN

- Reformer: The Efficient Transformer

- Deep Learning for Symbolic Mathematics

- And More...

πŸ‘‰ Creative Applications of Deep Learning with TensorFlow

Taught by: Parag Mital

About this Course: 

A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.

Review of User Who had Completed this Course:

"After taking several courses in Machine Learning, I came across this course and it immediately caught my attention due to the the speed of delivery, content topics and it's pace when talking about concepts such as gradient descent and convolutions. Honestly, the course truly is EXCELLENT. Parag really is great a presenting the materials in an easy-to-understand manner, and perhaps more importantly, he has you focus on the RIGHT concepts and not going down rabbit-holes."

πŸ‘‰ AI Institute “Geometry of Deep Learning” 

Created by: Microsoft

About this Course: 

Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning. Our three-day workshop stems on what we identify as the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning. We invite the leaders in these fields to bolster new collaborations and to look for new angles of attack on the mysteries of deep learning.

πŸ‘‰ Frontiers of Deep Learning

Created by: Simons Institute

About this Course: 

Classical theory that guides the design of nonparametric prediction methods like deep neural networks involves a tradeoff between the fit to the training data and the complexity of the prediction rule. Deep learning seems to operate outside the regime where these results are informative, since deep networks can perform well even with a perfect fit to noisytraining data. We investigate this phenomenon of 'benign overfitting' in the simplest setting, that of linear prediction. We give a characterization of linear regression problems for which the minimum norm interpolating prediction rule has near-optimal prediction accuracy. The characterization is in terms of two notions of effective rank of the data covariance. It shows that overparameterization is essential: the number of directions in parameter space that are unimportant for prediction must significantly exceed the sample size.  We discuss implications for deep networks and for robustness to adversarial examples.

πŸ‘‰ Deep Learning by Intel

Created by: Intel

About this Course:

By the end of this course, students will have a firm understanding of:

- Techniques, terminology, and mathematics of deep learning

- Fundamental neural network architectures, feedforward networks, convolutional networks, and recurrent networks

- How to appropriately build and train these models

- Various deep learning applications

- How to use pre-trained models for best results

πŸ‘‰ Emerging Challenges in Deep Learning

Taught by: Chris Manning and Other Experts

About this Course: 

The topics covered in this course are

Knowledge is embedded in language neural networks but can they reason?

- Machine Learning-based Design of Proteins and Small Molecules

- Efficient Deep Learning with Humans in the Loop

- Aligning ML objectives with human values

- Flexible Neural Networks and the Frontiers of Meta-Learning

- Reinforcement Learning in Feature Space: Complexity and Regret

- Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes

πŸ‘‰ New Deep Learning Techniques 

Taught by: Sam Bowman, Yann Lecun and Other Experts

About this Course: 

This course will bring together experts in mathematics (statistics, harmonic analysis, optimization, graph theory, sparsity, topology), machine learning (deep learning, supervised & unsupervised learning, metric learning) and specific applicative domains (neuroscience, genetics, social science, computer vision) to establish the current state of these emerging techniques and discuss the next directions.

Tensorflow, Reinforcement Learning, General Adversarial Networks and Natural Language Processing

Tensorflow, Reinforcement Learning, General Adversarial Networks and Natural Language Processing

πŸ‘‰ Getting Started with Tensorflow 2

Taught by: Dr Kevin Webster

About this Course: 

By the end of this project type course you will be introduced to Tensorflow from the ground up. Tensorflow is an extremely popular free and open source machine learning framework developed by Google. Using Tensorflow, developers can utilize the power of deep learning to create applications with features of artificial intelligence, such as natural language processing, object/image recognition, and prediction of continuous variables. We'll practice the basics, including installation, and finally we will build our own simple Tensorflow model. Your Tensorflow journey begins here!

πŸ‘‰ Machine Learning Crash Course with TensorFlow APIs

Created by: Google

About this Course: 

- Learn best practices from Google experts on key machine learning concepts.

- How does machine learning differ from traditional programming?

- What is loss, and how do I measure it?

- How does gradient descent work?

- How do I determine whether my model is effective?

- How do I represent my data so that a program can learn from it?

- How do I build a deep neural network?

Review of User Who had Completed this Course:

For a 15 hours course (it took me far longer than that), it does pretty well on presenting the basic theory necessary to apply it in easy but still increasingly complex exercises. It works on their own servers, so no installation is required, and it is based on Python while using the TensorFlow library (which makes sense, being both from Google).

πŸ‘‰ Practical Machine Learning with Tensorflow

Taught by: Prof. Ashish Tendulkar and Prof. Balaraman Ravindran

About this Course: 

This will be an applied Machine Learning Course jointly offered by Google and IIT Madras. We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow. INTENDED AUDIENCE: Any Interested Candidates PREREQUISITES: Programming in Python, Data Mining or Machine Learning or Data Science.

πŸ‘‰ Reinforcement Learning by Sentdex

Created by: Sentdex

About this Course: 

Welcome to a reinforcement learning tutorial. In this part, we're going to focus on Q-Learning.

Q-Learning is a model-free form of machine learning, in the sense that the AI "agent" does not need to know or have a model of the environment that it will be in. The same algorithm can be used across a variety of environments.

For a given environment, everything is broken down into "states" and "actions." The states are observations and samplings that we pull from the environment, and the actions are the choices the agent has made based on the observation. For the purposes of the rest of this tutorial, we'll use the context of our environment to exemplify how this works.

πŸ‘‰ CS234: Reinforcement Learning 

Taught by: Professor Emma Brunskill, Stanford University

About this Course: 

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning techniques with reinforcement learning.

πŸ‘‰ Reinforcement Learning

Offered by: Georgia Tech

About this Course: 

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Review of User Who had Completed this Course:

I think this course is awesome! The constant interaction between both professors really clarifies concepts and helps to avoid some bias about certain topics. However, as another reviewer, this course is not meant for beginners in the RL domain. I recommend taking Prof. David Silver's free online lectures before this course. I am experimenting that mixture and both courses complement superbly. This course is a more advanced and fast-paced course in RL compared to standard RL literature. At the begging it seems challenging but they encourage you to think about the problem and not just giving you all the solutions. I really recommend this course if you want to do research in RL and then Deep Reinforcement learning fields.

πŸ‘‰ Introduction to Reinforcement Learning

Taught by: David Silver

About this Course: 

This lecture series, taught at University College London by David Silver - DeepMind Principal Scientist, UCL professor and the co-creator of AlphaZero - will introduce students to the main methods and techniques used in RL. Students will also find Sutton and Barto’s classic book, Reinforcement Learning: an Introduction a helpful companion.

πŸ‘‰ Workshop on New Directions in Reinforcement Learning and Con

Created by: Institute of Advanced Study

About this Course: 

The course covers this topic

Curiosity, unobserved rewards and function Approximation in RL

- Understanding deep networks and its role played in prioritized search

- Towards Structural Risk Minimization for RL

- Robust Control with Perception in the Loop: Towards Open-World Manipulation

- Unsupervised state embedding and aggregation towards scalable reinforcement learning

- The Non-Stochastic Control Problem

- Deep Reinforcement Learning in the Real World

- And More...

πŸ‘‰ Natural Language Processing

Created by: Kaggle

About this Course: 

Distinguish yourself by learning to work with text data. Topics covered in this course are

1. Intro to NLP - Get started with NLP.

2. Text Classification - Combine machine learning with your newfound NLP skills.

3. Word Vectors - Explore an idea that ushered in a new generation of NLP techniques.

πŸ‘‰ CS224N: Natural Language Processing with Deep Learning by Stanford

Created by: Stanford University

About this Course: 

Lectures covered in this course are

Lecture 1: Natural Language Processing with Deep Learning

Lecture 2: Word Vector Representations: word2vec

Lecture 3: GloVe: Global Vectors for Word Representation

Lecture 4: Word Window Classification and Neural Networks

Lecture 5: Backpropagation and Project Advice

Lecture 6: Dependency Parsing

Lecture 7: Introduction to TensorFlow

Lecture 8: Recurrent Neural Networks and Language Models

Lecture 9: Machine Translation and Advanced Recurrent LSTMs and GRUs

And More...

πŸ‘‰ Stanford CS224U: Natural Language Understanding 

Taught by: Christopher Potts and Bill MacCartney

About this Course: 

What you will learn?

- Distributed word representations

- Relation extraction with distant supervision

- Natural language inference

- Supervised sentiment analysis

- Grounded language understanding

- Semantic parsing

- Contextual word representations (including updated coverage of BERT, RoBERTa, - ELECTRA, and XLNet)

- Evaluation methods and metrics

πŸ‘‰ Machine Translation and Natural Language Processing (NLP)

Created by: Amazon

About this Course: 

These courses explore how machines interact with the human language. We’ll cover AWS services that help you with neural networks and natural language processing topics like automatic speech recognition, natural and fluent language translation, and insights and relationships in text.

Review of User Who had Completed this Course:

- Entire Course contents are very informative and helped in gaining good understanding.

- Provides awareness about all amazon web services which are necessary for working on NLP based applications.

πŸ‘‰ Apply Generative Adversarial Networks (GANs)

Taught by: Sharon Zhou

About this Course: 

In this course, you will:

- Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity

- Leverage the image-to-image translation framework and identify applications to modalities beyond images

- Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa)

- Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures

- Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one

Reviews of Users Who had Completed this Course:

I really liked the exposure to preparing various loss functions in paired and non-paired GANs, introduction to other applications, and many great changes to improve the quality of the networks!

- Overall, this is an excellent course. The content is high quality and compact. The course is highly recommended for professionals who have limited time to keep up with the state-of-the-art in GANs. I feel that the course has given me enough knowledge for me to find ways to apply these skills for good in the industry.

Libraries, Frameworks, Algorithms and Other Intermediate & Advanced Level Machine Learning Courses

Libraries, Frameworks, Algorithms and Other Intermediate & Advanced Level Machine Learning Courses

πŸ‘‰ Computer Vision

Created by: Kaggle

About this Course: 

Create image classifiers with TensorFlow and Keras, and explore convolutional neural networks. Inside this course, you will learn this topics

1. The Convolutional Classifier - Create your first computer vision model with Keras.

2. Convolution and ReLU - Discover how convnets create features with convolutional layers.

3. Maximum Pooling - Learn more about feature extraction with maximum pooling.

4. The Sliding Window - Explore two important parameters: stride and padding.

5. Custom Convnets - Design your own convnet.

6. Data Augmentation - Boost performance by creating extra training data.

~ Bonus Lessons

Create Your First Submission - Use Kaggle's free TPUs to make a submission to the Petals to the Metal competition!

Getting Started: TPUs + Cassava Leaf Disease - Use Kaggle's free TPUs to make a submission to the Cassava Leaf Disease Classification competition.

πŸ‘‰ Machine Learning: Unsupervised Learning

Taught by: Charles Isbell and Michael Littman

About this Course: 

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data.

πŸ‘‰ Deep Unsupervised Learning

Taught by: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas

About this Course: 

About: This course will cover two areas of deep learning in which labeled data is not required: Deep Generative Models and Self-supervised Learning. Recent advances in generative models have made it possible to realistically model high-dimensional raw data such as natural images, audio waveforms and text corpora. Strides in self-supervised learning have started to close the gap between supervised representation learning and unsupervised representation learning in terms of fine-tuning to unseen tasks. This course will cover the theoretical foundations of these topics as well as their newly enabled applications.  

πŸ‘‰ Pandas

Created by: Kaggle

About this Course: 

Solve short hands-on challenges to perfect your data manipulation skills. In this course, you will learn about..

1. Creating, Reading and Writing -You can't work with data if you can't read it. Get started here.

2. Indexing, Selecting & Assigning - Pro data scientists do this dozens of times a day. You can, too!

3. Summary Functions and Maps - Extract insights from your data.

4. Grouping and Sorting - Scale up your level of insight. The more complex the dataset, the more this matters

5. Data Types and Missing Values - Deal with the most common progress-blocking problems

6. Renaming and Combining - Data comes in from many sources. Help it all make sense together

πŸ‘‰ Intro to SQL

Taught by: Kaggle

About this Course: 

Learn SQL for working with databases, using Google BigQuery to scale to massive datasets. In this course, you will learn about..

1. Getting Started With SQL and BigQuery - Learn the workflow for handling big datasets with BigQuery and SQL

2. Select, From & Where - The foundational compontents for all SQL queries

3. Group By, Having & Count - Get more interesting insights directly from your SQL queries

4. Order By - Order your results to focus on the most important data for your use case.

5. As & With - Organize your query for better readability. This becomes especially important for complex queries.

6. Joining Data - Combine data sources. Critical for almost all real-world data problems

πŸ‘‰ Spark

Created by: Insight

About this Course: 

In this course, you’ll learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark, the Python library for interacting with Spark. In the first lesson, you will learn about big data and how Spark fits into the big data ecosystem. In lesson two, you will be practicing processing and cleaning datasets to get comfortable with Spark’s SQL and dataframe APIs. In the third lesson, you will debug and optimize your Spark code when running on a cluster. In lesson four, you will use Spark’s Machine Learning Library to train machine learning models at scale.

πŸ‘‰ Fundamentals of TinyML

Created by: Harvard University

About this Course: 

The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.

πŸ‘‰ MLOps (Machine Learning Operations) Fundamentals

Taught by: Google Cloud Training

About this Course: 

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

Review of User Who had Completed this Course:

Thank You, Coursera & Google, It was great session & learn some practical Aspects & fundamentals of ML. I hope it will help me in the future. Thank You.

πŸ‘‰ CatBoost vs XGBoost - Quick Intro and Modeling Basics

Taught by: Manuel Amunategui

About this Course: 

XGBoost is one of the most powerful boosted models in existence until now... here comes CatBoost. Let's explore how it compares to XGBoost using Python and also explore CatBoost on both a classification dataset and a regression one. Let's have some fun!

- Part 1

We're going to start by unleashing XGBoost and CatBoostost on an independent data set version of the Titanic - the ship's manifest of those that did and didn't survive the tragic sinking of the ship in the North Atlantic Ocean. It happened in 1912 after hitting an iceberg on its maiden voyage to New York. You probably have already used it as it is extremely predictive, basically, women, children and the rich survived while men and the poor mostly didn't.

- Part 2

In the second part, we'll model a linear regression and classification on the titanic for classification and the Boston housing data.I'll also introduce you to a cool tool - Pandas Profiler for quick EDAs.

πŸ‘‰ Statistical Machine Learning

Taught by: Ulrike von Luxburg

About this Course: 

The course covers the standard paradigms and algorithms in statistical machine learning, simply browse through the titles of the individual lectures to get an impression of the contents. 

πŸ‘‰ Anomaly Detection

Created by: Intel

About this Course: 

Learn how to use statistics and machine learning to detect anomalies in data. As a fundamental part of data science and AI theory, the study and application of how to identify abnormal data can be applied to supervised learning, data analytics, financial prediction, and many more industries. Understanding the theory and intuition behind these methods is an essential part of the modern developer's and researcher’s tools and knowledge base.

πŸ‘‰ Predictive Analytics using Machine Learning

Taught by: Dr Johannes De Smedt

About this Course: 

This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.

The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.

πŸ‘‰ Intermediate Machine Learning Course

Created by: Kaggle

About this Course: 

Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful. In this course you will learn about

1. Introduction - Review what you need for this Micro-Course

2. Missing Values - Missing values happen. Be prepared for this common challenge in real datasets.

3. Categorical Variables - There's a lot of non-numeric data out there. Here's how to use it for machine learning

4. Pipelines - A critical skill for deploying (and even testing) complex models with pre-processing

5. Cross-Validation - A better way to test your models

6. XGBoost -The most accurate modeling technique for structured data

7. Data Leakage - Find and fix this problem that ruins your model in subtle ways

😲 Bonus Lessons

Get Started: Feb Tabular Playground Competition - Apply what you have learned in this course to a beginner-friendly competition.

πŸ‘‰ Stanford CS330: Deep Multi-Task and Meta Learning

Taught by: Chelsea Finn

About this Course: 

The course will include live lectures over zoom, three homework assignments, a fourth optional homework assignment, and a course project. The lectures will discuss the fundamentals of topics required for understanding and designing multi-task and meta-learning algorithms in both supervised learning and reinforcement learning domains. The assignments will focus on coding problems that emphasize these fundamentals. Finally, students will present a short spotlight of their project proposal and, at the end of the quarter, their completed projects.

πŸ‘‰ Deep RL Bootcamp

Taught by: Pieter Abbeel and other Professors

About this Course: 

Topics covered inside this course are

- Deep RL Bootcamp Lecture 1: Motivation + Overview + Exact Solution Methods

- Deep RL Bootcamp Lecture 2: Sampling-based Approximations and Function Fitting

- Deep RL Bootcamp Lecture 3: Deep Q-Networks

- Deep RL Bootcamp Lecture 4: Policy Gradients

- Deep RL Bootcamp Lecture 5: Natural Policy Gradients, TRPO, PPO

- Deep RL Bootcamp Lecture 6: Nuts and Bolts of Deep RL Experimentation

- Deep RL Bootcamp Lecture 7 SVG, DDPG, and Stochastic Computation Graphs (John Schulman)

- And More...

πŸ‘‰ Deep Bayes

Taught by: Dmitry Vetrov and Ekaterina Lobacheva

About this Course: 

You'll learn about...

- Introduction to Bayesian methods

- Bayesian reasoning

- Variational inference

- Latent variable models and EM-algorithm

- Approximate Bayesian inference

- Stochastic variational inference and variational autoencoders

- Variational autoencoders

- General Adversarial Network

- And More...

πŸ‘‰ Sparse Representations in Image Processing: From Theory to Practice

Taught by: Michael Elad and Yaniv Romano

About this Course: 

In this course, you will learn how to use sparse representations in series of image processing tasks. We will cover applications such as denoising, deblurring, inpainting, image separation, compression, super-resolution, and more. A key feature in migrating from the theoretical model to its practical deployment is the adaptation of the dictionary to the signal. This topic, known as "dictionary learning" will be presented, along with ways to use the trained dictionaries in the above mentioned applications.

Review of User Who had Completed this Course:

An excellent and very interesting course at an advanced level. Familiarity with linear algebra is essential and general familiarity with image and signal processing is also assumed. The accompanying theoretical course is also very well presented and very interesting.

πŸ‘‰ Applications of TinyML

Taught by: Vijay Janapa Reddi and Laurence Moroney

About this Course: 

Chapters covered in this course

Chapter 1.1: Welcome to Applications of TinyML

Chapter 1.2: AI Lifecycle and ML Workflow

Chapter 1.3: Machine Learning on Mobile and Edge IoT Devices - Part 1

Chapter 1.4: Machine Learning on Mobile and Edge IoT Devices - Part 2

Chapter 1.5: Keyword Spotting

Chapter 1.6: Data Engineering for TinyML Applications

Chapter 1.7: Visual Wake Words

Chapter 1.8: Anomaly Detection

Chapter 1.9: Responsible AI Development

Chapter 1.10: Summary

πŸ‘‰ Exploratory Data Analysis for Machine Learning

Taught by: Mark J Grover and Miguel Maldonado

About this Course: 

By the end of this course you should be able to:

- Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud 

- Describe and use common feature selection and feature engineering techniques

- Handle categorical and ordinal features, as well as missing values

- Use a variety of techniques for detecting and dealing with outliers

- Articulate why feature scaling is important and use a variety of scaling techniques

Reviews of User Who had Completed this Course:

- If you really make the exercises and the final assignment the course really contributes you to better understand Data Analysis

- I really liked, that you need to spend time on the independent work which consists of data preprocessing, EDA, and hypothesis testing.

πŸ‘‰ Data-Driven Dynamical Systems with Machine Learning

Taught by: Steve Brunton

About this Course: 

Modeling complex systems from data is an age old pursuit.  Machine learning is rapidly improving our ability to build dynamical systems models from data.  This lecture series will cover many leading modern methods.  

This lecture series will go into depth on data-driven control, following Chapter 7 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control"  by Brunton and Kutz

πŸ‘‰ Mining Massive Datasets - Stanford University

Created by: Stanford University

About this Course: 

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general.  The rest of the course is devoted to algorithms for extracting models and information from large datasets.  Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.  We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair.  When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches.  Many other large-scale algorithms are covered as well, as outlined in the course syllabus.

πŸ‘‰ Advanced Machine Learning

Taught by: Joachim M Buhmann

About this Course:

Lectures covered in this course are

- Introduction - Advanced Machine Learning

- Philosophical Motivation 

- Data-driven learning 

- Recursive Bayesian Estimation 

- Support Vector Machines 

- Nonlinear SVMs 

- Approximately Correct Learning 

- Boosting

- VC Inequality and Uniform Convergence

- And More..

πŸ‘‰ Advanced SQL

Created by: Kaggle

About this Course: 

What Lessons you will learn?

1. JOINs and UNIONs - Combine information from multiple tables.

2. Analytic Functions - Perform complex calculations on groups of rows.

3. Nested and Repeated Data - Learn to query complex datatypes in BigQuery.

4. Writing Efficient Queries - Write queries to run faster and use less data.

πŸ‘‰ Advanced Deep Learning & Reinforcement Learning

Created by: DeepMind

About this Course: 

This course, taught originally at UCL and recorded for online access, has two interleaved parts that converge towards the end of the course. One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. The two strands come together when we discuss deep reinforcement learning, where deep neural networks are trained as function approximators in a reinforcement learning setting.

The deep learning stream of the course will cover a short introduction to neural networks and supervised learning with TensorFlow, followed by lectures on convolutional neural networks, recurrent neural networks, end-to-end and energy-based learning, optimization methods, unsupervised learning as well as attention and memory. Possible applications areas to be discussed include object recognition and natural language processing.

πŸ‘‰ Apply Creative Machine Learning

Taught by: Molly Bridge

About this Course: 

On this course, you’ll learn the core concepts of what machine learning is, and how machine learning works. You’ll learn how to build simple classification systems that can discriminate between different types of information, and regressions systems that can map interactions onto different outputs, like sliders on a synthesiser.

You’ll explore the full extent of machine learning systems’ abilities, specifically in relation to the creative industries.

πŸ‘‰ Developing Machine Learning Applications

Created by: Amazon

About this Course: 

In this curriculum, we’ll explore Amazon’s fully managed ML platform, Amazon SageMaker. Specifically, we’ll discuss how to train and tune models, how certain algorithms are built in, how you can bring your own algorithm, and how to build for particular use cases like recommender systems or anomaly detection.

πŸ‘‰ Structuring Machine Learning Projects

Taught by: Andrew Ng

About this Course: 

Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.

After 2 weeks, you will:

- Understand how to diagnose errors in a machine learning system, and

- Be able to prioritize the most promising directions for reducing error

- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance

- Know how to apply end-to-end learning, transfer learning, and multi-task learning

Review of User Who had Completed this Course:

The course covered a range of practical issues, such as creating a single performance metric to quickly compare algorithms, how to compare the algorithm with human error to estimate Bayes (ideal) error rates and how to manually inspect and analyze errors to decide on further improvements to the algorithm.

This course was the first one in the series to have no programming assignments, opting instead for a quiz at the end of each week presented as a 45-minute case study or "flight simulator". The idea behind these "flight simulators" was to present the student with more complex, long term issues a practitioner would encounter over the course of a real-world machine learning project.

πŸ‘‰ Data Science and Machine Learning Capstone Project

Taught by: Sourav Mazumder and Linda Liu

About this Course: 

In this capstone project, you’ll explore data sets in New York’s 311 system, which is used by New Yorkers to report complaints for the non-emergency problems they face. Upon being reported, various agencies in New York get assigned to resolve these problems. The data related to these complaints is available in the New York City Open Dataset. On investigation, one can see that in the last few years the 311 complaints coming to the Department of Housing Preservation and Development in New York City have increased significantly.

Your task is to find out the answers to some of the questions that would help the Department of Housing Preservation and Development in New York City effectively tackle the 311 complaints coming to them. You will need to use the techniques you learned in your previous Python, data science, and machine learning courses, including data ingestion, data exploration, data visualization, feature engineering, probabilistic modeling, model validation, and more.

πŸ‘‰ CS155 - Machine Learning & Data Mining by Caltech University

Taught by: Yisong Yue 

About this Course:

Lectures covered in this course are

Lecture 1: Administrivia & Basics

Lecture 2: Perceptron & Gradient Descent

Lecture 3: Loss Functions

Lecture 4: Regularization

Lecture 5: Decision Trees, Bagging, Random Forests

Lecture 6: Boosting & Ensemble Selection

Lecture 7: Deep Learning

Generative Models and Naive Bayes, Hidden Markov Models, Clustering & Dimensionality Reduction and More...

πŸ‘‰ Machine Learning Exam Basics

Taught by: Amazon

About this Course: 

Throughout this curriculum we’ll explore the AWS machine learning services that enable everything from building, training, and deploying models at scale, to deep learning. We’ll hear from some of Amazon’s own machine learning scientists about how to consider machine learning business challenges and decisions. Finally, we’ll show you how data is moved and processed throughout the whole machine learning pipeline.

πŸ‘‰ Machine Learning Interview Preparation

Taught by: Arpan Chakraborty, Brynn Claypoole and Horatio Thomas

About this Course: 

In this course, you’ll learn exactly what to expect during a machine learning interview. You’ll cover all the common questions and technical strategies, and review a range of important topics, from machine learning algorithms to image categorization. You’ll also learn best practices for data structure questions and whiteboard problems, and at the end of the course, you’ll get unlimited access to mock interviews on Pramp. Complete this course and hone your interview skills today!

πŸ‘‰ Data Science: Machine Learning

Taught by: Rafael Irizarry

About this Course: 

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

Review of User Who had Completed this Course:

The climax Course of the Data Science Series from Harvard.

All in all, good. But some important issues prevented the Course from being top notch:

- some obvious messiness in the Course: assignments on some topics whereas they were not mentioned in the Course content (PCA, Clustering)

- some bugs in the assignments

- some important algos not included in the Course content: SVM, Boosting
The other Courses were almost bug free. So this is disapointing

πŸ‘‰ Data Science: Machine Learning and Predictions

Taught by: Ani Adhikari, John DeNero and David Wagner

About this Course: 

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

πŸ‘‰ Machine Learning for Musicians and Artists by University of London 

Taught by: Rebecca Fiebrink

Specific topics of discussion include:

• What is machine learning?

• Common types of machine learning for making sense of human actions and sensor data, with a focus on classification, regression, and segmentation

• The “machine learning pipeline”: understanding how signals, features, algorithms, and models fit together, and how to select and configure each part of this pipeline to get good analysis results

• Off-the-shelf tools for machine learning (e.g., Wekinator, Weka, GestureFollower)

• Feature extraction and analysis techniques that are well-suited for music, dance, gaming, and visual art, especially for human motion analysis and audio analysis

• How to connect your machine learning tools to common digital arts tools such as Max/MSP, PD, ChucK, Processing, Unity 3D, SuperCollider, OpenFrameworks

• Introduction to cheap & easy sensing technologies that can be used as inputs to machine learning systems (e.g., Kinect, computer vision, hardware sensors, gaming controllers)

Reviews of Users Who have Taken this Course:

- In my case I learned how to transform a multi-variable stream of electric-power monitoring data into inputs for an Ableton Live composition. I'm planning to add a stream of weather data and another stream of live game-cameras (using photo-recognition) to the mix. This class gave me the confidence to take on that great hack. :-)

- Pragmatically, this course gives you the tools to introduce meaningful gestural control or input to digital music (my interest) as well as a range of other applications which emerge in the course and from the forums.
The software tools provided are accessible with good quality free options as well as well-known paid-for options. The teaching material is top-notch and the most exciting part for me is the way that different machine learning approaches are illustrated in a very accessible way, and I have done the straight math versions too ;)

That's it for today. Hope this article will be helpful to you. If you think this list of best free machine learning courses can help others then please share it with the needed ones as well as with your friends. Also stay tuned with us as this list will be updated when new as well as other free machine learning courses and free artificial intelligence course will be available. Thanks for reading till the end. And if you have any free machine learning course that is not available in this post, please share it with us on any of our social media accounts. We'll verify it and add it in this list.
February 10, 2021


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