AssemblyAI-Community/ML-Study-Guide is ranked 9,362 among these 8 peers; ray-project/llm-applications is ranked 9,349.

How to get started with ML This my recommended study guide to get started with Machine Learning. Focus only on these topics, then come back later in case you need to learn more. Khan Academy - Multivariable Calculus Khan Academy - Differential Equations Khan Academy - Linear Algebra Khan Academy - Statistics Probability Optional: 3Blue1Brown - Essence of Linear Algebra 2. Learn Python 4h Beginner Course 6h Intermediate Python Programming Course 3. Learn The ML Tech Stack: NumPy: 1h NumPy Crash Course Pandas: 1h Pandas

Crash Course Matplotlib: 1h Matplotlib Crash Course Course (Scikit-Learn and TensorFlow are taught in step 4. PyTorch is optional, maybe in step 7) 4. Machine Learning Courses Machine Learning Specialization Andrew Ng | Coursera (3 Courses) Optional: Machine Learning From Scratch 5. Hands-on Data Preparation Kaggle Intro to Machine Learning Kaggle Intermediate Machine Learning 6. Solve Challenges and build your own projects with datasets from Kaggle. Specialize & Create Blog Specialize in one field (e. ) Look at requirements in corresponding

job descriptions and learn those skills Tip: Create a blog and share tutorials and what you have learned! Books If you prefer learning with books, these are great recommendations: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Machine Learning with PyTorch and Scikit-Learn