Table Of Contents

- What are Some of the Best Machine Learning Courses? (Our Favorite from the List)
- Free Courses To Learn Machine Learning (Introductory Courses)
- Machine Learning Course for Beginners
- Intro to Machine Learning
- Machine Learning Course by Stanford University
- Machine Learning Foundation
- Introduction to Machine Learning
- Introduction to Machine Learning Course
- A Gentle Introduction to Machine Learning
- Machine Learning Fundamentals
- Machine Learning from Data
- Machine Learning Explanability
- Introduction to Machine Learning for Coders!
- Theoretical Machine Learning Lecture Series
- 50 Must Know Concepts, Algorithms in Machine Learning
- CSEP 546 – Machine Learning
- Introduction to Machine Learning
- Foundations of machine learning and statistical inference
- Machine Learning Fundamentals
- Applied Machine Learning
- Introduction to Machine Learning
- CS229: Machine Learning
- Fundamentals of Machine Learning
- FA18: Machine Learning
- Introduction to Machine Learning
- Learning from Data (Introductory Machine Learning course) by California Institute of Technology
- Introduction to Machine Learning (IIT KGP)
- In-depth Introduction To Machine Learning In 15 Hours Of Expert Videos
- Real World Machine Learning Case Study
- Machine Learning
- Machine Learning
- Foundations of Machine Learning
- Machine Learning
- Introduction to Machine Learning and Pattern Recognition
- Microsoft AI Classroom
- Artificial Intelligence
- Welcome to Artificial Intelligence!
- Introduction to Artificial Intelligence
- Demystifying AI/ML/DL
- CS221 Artificial Intelligence Course

- Free Courses To Learn Machine Learning with Python
- Free Courses To Learn Mathematics for Machine Learning
- Mathematics For Machine Learning (MIT)
- Mathematics For Machine Learning by Microsoft Research
- Linear Algebra For Machine Learning
- Mathematics for Machine Learning: Multivariate Calculus
- Probability and Statistics For Machine Learning
- Probabilistic Machine Learning
- Essential Mathematics for Machine Learning
- MIT RES.LL-005 Mathematics of Big Data and Machine Learning
- Computational Linear Algebra for Coders
- Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

- Free Courses To Learn Neural Networks for Machine Learning
- Neural Networks for Machine Learning
- A Small Playlist on Neural Networks
- CS231N: Convolutional Neural Networks for Visual Recognition by Stanford
- IBM Introduction to Deep Learning & Neural Networks with Keras
- CMU Neural Nets for NLP 2020
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Neural Network Programming – Deep Learning with PyTorch
- Fuzzy Logic and Neural Networks

- Free Courses To Learn Deep Learning
- Introduction to Deep Learning
- Intro to Deep Learning
- Practical Deep Learning for Coders
- Intro to TensorFlow for Deep Learning
- Full Stack Deep Learning
- Deep Learning With Tensorflow 2.0, Keras and Python
- Deep Learning (with PyTorch)
- Deep Learning for NLP at Oxford with Deep Mind
- Deep Learning Architectures
- Creative Applications of Deep Learning with TensorFlow
- AI Institute “Geometry of Deep Learning”
- Frontiers of Deep Learning
- Deep Learning by Intel
- Emerging Challenges in Deep Learning
- New Deep Learning Techniques

- Free Courses To Learn TensorFlow, Reinforcement Learning, General Adversarial Networks and Natural Language Processing
- Getting Started with Tensorflow 2
- Machine Learning Crash Course with TensorFlow APIs
- Practical Machine Learning with Tensorflow
- Reinforcement Learning by Sentdex
- CS234: Reinforcement Learning
- Reinforcement Learning
- Introduction to Reinforcement Learning
- Workshop on New Directions in Reinforcement Learning and Con
- Natural Language Processing
- CS224N: Natural Language Processing with Deep Learning by Stanford
- Stanford CS224U: Natural Language Understanding
- Machine Translation and Natural Language Processing (NLP)
- Apply Generative Adversarial Networks (GANs)

- Free Courses To Learn Libraries, Frameworks, Specific Part Of ML And Various Types Of Machine Learning Algorithms
- Computer Vision
- Machine Learning: Unsupervised Learning
- Deep Unsupervised Learning
- Pandas
- Intro to SQL
- Spark
- Fundamentals of TinyML
- MLOps (Machine Learning Operations) Fundamentals
- CatBoost vs XGBoost – Quick Intro and Modeling Basics
- Statistical Machine Learning
- Anomaly Detection
- Predictive Analytics using Machine Learning
- Intermediate Machine Learning Course
- Stanford CS330: Deep Multi-Task and Meta Learning
- Deep RL Bootcamp
- Deep Bayes
- Sparse Representations in Image Processing: From Theory to Practice
- Applications of TinyML
- Exploratory Data Analysis for Machine Learning
- Data-Driven Dynamical Systems with Machine Learning
- Mining Massive Datasets – Stanford University
- Advanced Machine Learning
- Advanced SQL
- Advanced Deep Learning & Reinforcement Learning
- Apply Creative Machine Learning
- Developing Machine Learning Applications
- Structuring Machine Learning Projects
- Data Science and Machine Learning Capstone Project
- CS155 – Machine Learning & Data Mining by Caltech University
- Machine Learning Exam Basics
- Machine Learning Interview Preparation
- Data Science: Machine Learning
- Machine Learning for Musicians and Artists by University of London

In this post, we’ll share 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).

## What are Some of the Best Machine Learning Courses? (Our Favorite from the List)

- Machine Learning Course for Beginners (By FreeCodeCamp)
- Deep Learning (By Yann LeCunn And Alfredo Canziani)
- Introduction To Deep Learning (By MIT / Alexander Amini, Ava Soleimany, etc )
- Machine Learning Course (By Stanford University)
- Mathematics For Machine Learning (By MIT)
- Machine Learning with Python (By Sentdex)
- Neural Networks for Machine Learning (By Geoffrey Hinton)
- Machine Learning Crash Course with TensorFlow APIs (By Google)
- Computer Vision (By Kaggle)

**Note:** List is very big as we’ve covered courses for Machine Learning, Math for ML, Machine Learning with Python, Deep Learning, Machine Learning Libraries, Frameworks, Neural Networks and other ML topics. So,** We recommend you to check the Table Of Content first and go through all the titles. **

Starting with…

## Free Courses To Learn Machine Learning (Introductory Courses)

### 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

- Basics of Machine Learning
- Linear Regression & Regularization
- Logistic Regression & Performance Metrics
- Support Vector Machine
- And More.

### 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…

- How Models Work – The first step if you’re new to machine learning
- Basic Data Exploration – Load and understand your data
- Your First Machine Learning Model – Building your first model. Hurray!
- Model Validation – Measure the performance of your model ? so you can test and compare alternatives
- And More.

### 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).

### 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.

### Introduction to Machine Learning

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
- And More

### 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.

### 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.

### Machine Learning Fundamentals

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.

### 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.

### 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

- Use Cases for Model Insights – Why and when do you need insights?
- Permutation Importance – What features does your model think are important?
- Partial Plots – How does each feature affect your predictions?
- SHAP Values – Understand individual predictions
- Advanced Uses of SHAP Values – Aggregate SHAP values for even more detailed model insights

### 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.

### 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.

### 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.

### 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
- And More

### 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;
- And More

### 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.

### 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.

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### Applied Machine Learning

Taught by: Andreas C. Müller

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.

### 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.

### 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.

### 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.

### 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

### 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.

### 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

### 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 naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms.

### 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

1: Introduction

2: Statistical Learning

3: Linear Regression

4: Classification

And More

### 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

### 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.

### 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.

### 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.

### 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.

### 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.

### 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

### 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.

### 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,

- What are prerequisites for learning AI?
- What is Road map to start Machine learning project(ML)
- How to choose the best programming language for AI ?
- How much Mathematical knowledge needed for AI ?
- Which is the best AI Engine/Tool/Framework for AI ? and so on..

### 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.

### 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).

### 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.

## Free Courses To Learn Machine Learning with Python

### Machine Learning with Python

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.

### 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.

### 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.

### 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.

### 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 and TensorFlow

### 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

### 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

## Free Courses To Learn Mathematics for Machine Learning

### Mathematics For Machine Learning (MIT)

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.

### Mathematics For 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.

### Linear Algebra For Machine Learning

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.

### 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.

### Probability and Statistics For Machine Learning

About this Course:

Topics covered in this course are

1: Introduction

2: Exploratory Data Analysis

3: Producing Data

4: Probability

5: Inference

### 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 Tübingen 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.

### 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.

### 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.

### 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.

### 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.

## Free Courses To Learn Neural Networks for Machine Learning

### 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.

### 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

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### 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.

### 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.

### 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

### 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.

### 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!

### 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.

## Free Courses To Learn Deep Learning

### Introduction 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

- A Single Neuron – Learn about linear units, the building blocks of deep learning.
- Deep Neural Networks – Add hidden layers to your network to uncover complex relationships.
- Stochastic Gradient Descent – Use Keras and Tensorflow to train your first neural network.
- And More.

### Intro to Deep Learning

Created by: MIT / Alexander Amini, Ava Soleimany and others

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!

### Practical Deep Learning for Coders

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.

### 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.

### 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

### 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

### 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.

### 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.

### 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
- And More…

### 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.

### 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.

### 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.

### 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

### 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

### 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.

## Free Courses To Learn TensorFlow, Reinforcement Learning, General Adversarial Networks and Natural Language Processing

### 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.

### 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?
- And More

### 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.

### 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.

### 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.

### 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.

### 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.

### 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
- And More…

### Natural Language Processing

Created by: Kaggle

About this Course:

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

- Intro to NLP – Get started with NLP.
- Text Classification – Combine machine learning with your newfound NLP skills.
- Word Vectors – Explore an idea that ushered in a new generation of NLP techniques.

### CS224N: Natural Language Processing with Deep Learning by Stanford

Created by: Stanford University

About this Course:

Lectures covered in this course are

a. Natural Language Processing with Deep Learning

b. Word Vector Representations: word2vec

c. GloVe: Global Vectors for Word Representation

d. Word Window Classification and Neural Networks

And More…

### 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
- And More

### 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.

### 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)
- And More

## Free Courses To Learn Libraries, Frameworks, Specific Part Of ML And Various Types Of Machine Learning Algorithms

### 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

- The Convolutional Classifier – Create your first computer vision model with Keras.
- Convolution and ReLU – Discover how convnets create features with convolutional layers.
- Maximum Pooling – Learn more about feature extraction with maximum pooling.
- And More

### 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!

### 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.

### 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..

- Creating, Reading and Writing -You can’t work with data if you can’t read it. Get started here.
- Indexing, Selecting & Assigning – Pro data scientists do this dozens of times a day. You can, too!
- Summary Functions and Maps – Extract insights from your data.
- And More.

### 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..

- Getting Started With SQL and BigQuery – Learn the workflow for handling big datasets with BigQuery and SQL
- Select, From & Where – The foundational compontents for all SQL queries
- Group By, Having & Count – Get more interesting insights directly from your SQL queries
- And More.

### 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. First, You will learn about big data and how Spark fits into the big data ecosystem. Next, 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.

### 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.

### 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.

### 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.

### 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.

### 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.

### 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.

### 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

- Introduction – Review what you need for this Micro-Course
- Missing Values – Missing values happen. Be prepared for this common challenge in real datasets.
- Categorical Variables – There’s a lot of non-numeric data out there. Here’s how to use it for machine learning
- Pipelines – A critical skill for deploying (and even testing) complex models with pre-processing
- And More

### 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.

### Deep RL Bootcamp

Taught by: Pieter Abbeel and other Professors

About this Course:

Topics covered inside this course are

- a. Motivation + Overview + Exact Solution Methods
- b. Sampling-based Approximations and Function Fitting
- c. Deep Q-Networks
- d. Policy Gradients
- e. And More…

### 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
- And More…

### 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.

### Applications of TinyML

Taught by: Vijay Janapa Reddi and Laurence Moroney

About this Course:

Chapters covered in this course

a. Welcome to Applications of TinyML

b. AI Lifecycle and ML Workflow

c. Machine Learning on Mobile and Edge IoT Devices – Part 1

d. Machine Learning on Mobile and Edge IoT Devices – Part 2

e. Keyword Spotting

And More

### 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

### 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.

### 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.

### 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
- And More..

### Advanced SQL

Created by: Kaggle

About this Course:

What Lessons you will learn?

- JOINs and UNIONs – Combine information from multiple tables.
- Analytic Functions – Perform complex calculations on groups of rows.
- Nested and Repeated Data – Learn to query complex datatypes in BigQuery.
- Writing Efficient Queries – Write queries to run faster and use less data.

### 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.

### 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.

### 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.

### 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.

### 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.

### CS155 – Machine Learning & Data Mining by Caltech University

Taught by: Yisong Yue

### 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.

### 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!

### 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.

### 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 “ML 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)

And More

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 course will be available on the internet. Thanks for reading till the end.