selvadurai / ml-study-guide

Minimal Machine Learning Study Plan

How to get started with ML

This my recommended study guide to get started with Machine Learning.

Watch the video on YouTube.

1. Math

Learn some math basics! 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. Practise!

Solve Challenges and build your own projects with datasets from Kaggle.com.

7. Specialize & Create Blog

• Specialize in one field (e.g. Computer Vision, NLP, etc.)
• 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