7 Mistakes You're Making Each Year As An AI/ML Practitioner

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Mistake 1: Jumping Straight to Neural Networks: - It's a mistake to dive directly into Neural Networks when learning machine learning.

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- Machine Learning fundamentals are crucial and differ from Deep Learning fundamentals. - Understanding classical ML techniques is important for job interviews and a deeper grasp of the technology.

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Mistake 2: Ignoring Algorithms and Data Structures: - Neglecting algorithms and data structures is a common mistake.

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- Although not frequently used in real jobs, they play a significant role in ML job interviews. - Coding problems assess problem-solving skills, even for ML positions, so ignoring them is detrimental.

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Mistake 3: Ignoring the Fundamental Math: - Despite popular libraries handling math in ML, understanding fundamental math is essential.

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- Deeper comprehension aids in debugging and developing new methods. - Encouragement to tackle math step-by-step and embrace curiosity for a logical and fascinating learning experience.

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Mistake 4: Having a Rigid Mindset: - Advises against a narrow focus on popular topics like LLMs without exploring diverse ML fields.

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- Highlights the variety in ML, including RL, evolutionary learning, and applications in different domains. - Encourages flexibility, openness, and finding unique interests rather than following current trends.

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Mistake 5: Overthinking: - Discourages overthinking about the "best" way to learn ML.

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- Emphasizes the importance of curiosity and recommends just getting started. - Acknowledges the challenges in learning ML but underscores the value of curiosity-driven exploration.

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Mistake 6: Playing Single-Player Mode: - Stresses the benefits of collaborative learning over solitary studying.

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- Suggests finding a motivated friend for joint studying, debugging, and project work. - Highlights the boost in productivity, enjoyment, and accountability that comes from studying with a partner.

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Mistake 7: Doing too Many Projects: - Caution against the misconception that numerous small projects outweigh a few quality ones.

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- Emphasizes the importance of challenging oneself with a single difficult project. - Quality over quantity is crucial for standing out in the learning process.