Machine Learning Fundamentals

• Part of: AI & Machine Learning — learning path 2 of 3
• Length: 4 chapters · 22 lessons · 12 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. Supervised Learning: Regression

• 6 lessons
• Skills: Linear Regression · Regularization · Feature Engineering · Cross-Validation

2. Supervised Learning: Classification

• 6 lessons
• Skills: Logistic Regression · Decision Trees · Random Forests · XGBoost · Hyperparameter Tuning

3. Unsupervised Learning

• 5 lessons
• Skills: k-means Clustering · Hierarchical Clustering · DBSCAN · PCA · Dimensionality Reduction

4. Introduction to Deep Learning

• 5 lessons
• Skills: Neural Networks · Backpropagation · PyTorch · TensorFlow · Keras
• Checkpoint: ML Practitioner Gate

Checkpoints

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

• ML Practitioner Gate: Train a classifier on a public dataset, beat a baseline by ≥5% on a held-out test set, document the choices. Train a small neural network from scratch (PyTorch or Keras), reproduce a benchmark within 10% of the published number.