Python & Mathematics for ML

• Part of: AI & Machine Learning — learning path 1 of 3
• Length: 4 chapters · 21 lessons · 10 hours a week
• Level: Intermediate

1. Python & Mathematics for ML4 chapters · 10 hours a week · 1 checkpoint
2. Machine Learning Fundamentals4 chapters · 12 hours a week · 1 checkpoint
3. ML Engineering & Deployment3 chapters · 8 hours a week · 1 checkpoint

Start this track

Syllabus

1. Python Scientific Stack

• 6 lessons
• Skills: NumPy · pandas · Jupyter · SciPy

2. Linear Algebra for ML

• 5 lessons
• Skills: Vectors · Matrices · Eigenvalues · SVD · PCA

3. Calculus & Optimization

• 5 lessons
• Skills: Derivatives · Gradients · Gradient Descent · Backpropagation

4. Probability & Statistics for ML

• 5 lessons
• Skills: Probability · Bayesian Reasoning · Probability Distributions · Maximum Likelihood Estimation
• Checkpoint: Math Foundations Gate

Checkpoints

To pass each checkpoint you hand in a piece of real work. What each one asks for:

• Math Foundations Gate: Implement linear regression from scratch in NumPy: derive the closed-form solution, fit a model, compute R². Run a Bayes-rule update on a small dataset and explain the prior/likelihood/posterior.