CS229: Machine Learning Course (Stanford University)¶

A comprehensive implementation of Stanford’s CS229 Machine Learning course using Python. This collection provides hands-on, code-first implementations of all major machine learning algorithms covered in the course.

🆕 Updated with actual lecture transcripts from Andrew Ng’s 2018 MIT course!
Real examples, explanations, and insights from the original lectures integrated into interactive notebooks.

📚 Course Overview¶

Instructor: Andrew Ng
Institution: Stanford University (Autumn 2018)
Source: Lecture transcripts + Official Syllabus
Focus: Foundational machine learning algorithms and theory
Implementation: Python with NumPy, scikit-learn, and modern ML libraries

What You’ll Learn¶

Supervised Learning: Regression, Classification, Neural Networks
Unsupervised Learning: Clustering, PCA, ICA
Learning Theory: Bias-variance, VC dimension, PAC learning
Optimization: Gradient descent variants, Newton’s method
Practical Skills: Feature engineering, debugging ML systems

🗂️ Lecture Structure¶

Part I: Supervised Learning¶

Lecture 1: Linear Regression¶

File: 01_linear_regression.ipynb
Source: Lecture 2 Transcript (Linear Regression lecture)

Topics:

Machine learning introduction and motivation
Portland Housing dataset example (from Craigslist)
Linear regression hypothesis and cost function
Gradient descent (batch, stochastic, mini-batch)
Normal equation (closed-form solution)
Feature scaling and normalization
Learning rate tuning

From the Lecture:

“Let’s say you want to predict or estimate the prices of houses. This is data from Portland, Oregon…” - Andrew Ng

Implementations:

Portland housing price prediction (real data from lecture!)
Gradient descent from scratch
Normal equation solver: θ = (XᵀX)⁻¹Xᵀy
Vectorized implementations
Learning rate comparison (α = 0.001, 0.01, 0.1, 0.5)
Multi-variate regression on California Housing

Key Equations:

Hypothesis: h_θ(x) = θᵀx
Cost: J(θ) = (1/2m)Σ(h_θ(x⁽ⁱ⁾) - y⁽ⁱ⁾)²
Update: θ := θ - α∇J(θ)
Normal Equation: θ = (XᵀX)⁻¹Xᵀy

Practice: 8 exercises covering implementation, optimization, and analysis

Lecture 3: Locally Weighted Regression¶

File: 03_locally_weighted_regression.ipynb [NEW!]
Source: Lecture 3 Transcript (LWR, Probabilistic Interpretation)

Topics:

Parametric vs Non-parametric learning algorithms
Locally weighted regression (LWR) algorithm
Weight functions and bandwidth parameter τ
Avoiding feature engineering
Curse of dimensionality
When to use LWR vs other methods

From the Lecture:

“If you have curved data… it’s quite difficult to find features. Is it √x, log(x), x³? What is the set of features that lets you do this? Locally weighted regression sidesteps all those problems.” - Andrew Ng

Implementations:

Complete LWR class from scratch
Gaussian weight function: w⁽ⁱ⁾ = exp(-(x⁽ⁱ⁾-x)²/(2τ²))
Weighted least squares: θ = (XᵀWX)⁻¹XᵀWy
Bandwidth comparison (τ = 0.1, 0.5, 1.0, 2.0)
Weight visualization for different query points
Comparison: Linear vs Polynomial vs LWR

Key Insights:

Non-parametric: Must keep training data around
Local fitting: Different θ for each prediction
Automatic: No feature engineering needed
Computational: O(n³) per prediction

Best For: ✓ Low dimensional data (n ≤ 5)
✓ Non-linear patterns
✗ High dimensions
✗ Real-time prediction

Lecture 2 & 4: Logistic Regression (Classification)¶

File: 02_logistic_regression.ipynb [ENHANCED!]
Source: Lectures 3-4 Transcripts (Logistic Regression, Newton’s Method)

Topics:

Why linear regression fails for classification
Binary classification problem
Logistic/sigmoid function
Decision boundaries (linear and non-linear)
Cost function for classification (cross-entropy)
Gradient descent for logistic regression
Newton’s Method (new!)
Multi-class classification (One-vs-All)
Regularization for logistic regression

From the Lecture:

“Probably by far the most commonly used classification algorithm… Linear regression is just not a good algorithm for classification.” - Andrew Ng

“Gradient ascent takes baby steps, takes a lot of iterations. Newton’s method allows you to take much bigger jumps - you might need only 10 iterations instead of 100 or 1000.” - Andrew Ng

New Implementations:

Newton’s Method from scratch:
- Second-order optimization
- Hessian computation: H = XᵀDX
- Update: θ := θ + H⁻¹∇ℓ(θ)
- Convergence comparison with gradient ascent
- 5-20x faster convergence!

When to Use What:

Gradient Ascent: Large n (> 10,000 features)
Newton’s Method: Small to medium n (< 10,000 features)
L-BFGS: Middle ground (used in sklearn)

Implementations:

Sigmoid function and properties
Binary classifier on breast cancer data
Decision boundary visualization
Multi-class classification on digits
Comparison with linear regression for classification

Key Equations:

Hypothesis: h_θ(x) = g(θᵀx) where g(z) = 1/(1+e⁻ᶻ)
Cost: J(θ) = -(1/m)Σ[y log(h_θ(x)) + (1-y)log(1-h_θ(x))]

Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC

Lecture 8: Regularization and Bias-Variance¶

File: 04_regularization.ipynb [ENHANCED!]

Topics:

Overfitting and underfitting
Regularization intuition
Ridge regression (L2)
Lasso regression (L1)
Elastic Net
Regularized logistic regression
Choosing regularization parameter λ

Demonstrations:

Polynomial overfitting example
Regularization path visualization
Cross-validation for λ selection
Feature selection with Lasso
Comparison of regularization methods

Key Concepts:

Bias-variance trade-off
Model complexity vs performance
Structural risk minimization

Lecture 5-6: Generative Learning Algorithms¶

File: 05_generative_models.ipynb [ENHANCED!]
Source: Lectures 5-6 Transcripts (GDA, Naive Bayes)

Topics:

Discriminative vs Generative learning paradigms
Bayes’ rule framework: P(y|x) = P(x|y)P(y)/P(x)
Gaussian Discriminant Analysis (GDA)
Multivariate Gaussian distribution
Covariance matrix visualization
Naive Bayes classifier
Laplace smoothing
Text classification and spam filtering

From the Lecture:

“Rather than looking at two classes and trying to find the separation, the algorithm looks at the classes one at a time.” - Andrew Ng

Multivariate Gaussian:

“The Gaussian is this familiar bell-shaped curve. A multivariate Gaussian is the generalization to vector-valued random variables.” - Andrew Ng

Implementations:

GDA from scratch with MLE
Multivariate Gaussian visualization (μ and Σ effects)
Naive Bayes for spam detection
Laplace smoothing demonstration
Text classification with Multinomial vs Bernoulli event models

Comparison:

Logistic Regression vs GDA
When to use generative models

Lecture 6-7: Support Vector Machines¶

File: 06_svm.ipynb [ENHANCED!]
Source: Lectures 6-7 Transcripts (SVM, Kernels)

Topics:

Optimal margin classifier
Functional and geometric margins
Representer theorem
Primal and dual formulation
The Kernel Trick (new!)
Common kernels (Linear, Polynomial, RBF)
Working in infinite-dimensional feature spaces
Soft margin (slack variables)

From the Lecture:

“Support vector machine is one of my favorite algorithms - very turnkey, very widely applicable.” - Andrew Ng

“We can work in 100,000 dimensional, or a million dimensional, or 100 billion dimensional, or even infinite-dimensional feature spaces.” - Andrew Ng

New Theory:

Representer Theorem: w = Σ αᵢy⁽ⁱ⁾x⁽ⁱ⁾
- Even in infinite dimensions, only need to store m coefficients!
Kernel Trick: Never compute φ(x) explicitly
- Use K(x,z) = ⟨φ(x), φ(z)⟩ instead
- Example: Polynomial kernel K(x,z) = (xᵀz + 1)ᵈ
- RBF kernel: K(x,z) = exp(-γ||x-z||²)

Implementations:

Linear SVM with dual formulation
Kernel SVM (polynomial, RBF)
Non-linearly separable data (circles, moons)
Hyperparameter tuning (C, γ)
Decision boundary visualization
Support vector identification
RBF kernel deep dive (gamma effects)

Applications:

Image classification
Text categorization
Bioinformatics

Lecture 8: Regularization and Bias-Variance¶

File: 04_regularization.ipynb [ENHANCED!]
Source: Lecture 8 Transcript (Bias-Variance Tradeoff)

Topics:

Bias-variance tradeoff theory (new!)
Overfitting and underfitting from theoretical perspective
Ridge regression (L2)
Lasso regression (L1)
Elastic Net
Choosing regularization parameter λ

From the Lecture:

“Bias and variance is one of those concepts that’s easy to understand but hard to master. I’ve had PhD students that worked with me for several years, and their understanding continues to deepen.” - Andrew Ng

New Theoretical Framework:

High Bias (Underfitting): “Strong preconceptions that don’t match reality”
- Example: Fitting linear to curved data
- Model too simple
High Variance (Overfitting): “Predictions vary wildly with different datasets”
- Example: 5th-order polynomial through noisy points
- Model too complex
Just Right: Captures true pattern, generalizes well

Workflow from Lecture:

Train quick/dirty baseline
Identify: High bias or high variance?
Apply appropriate fix:
- High bias → Add features, more complexity, decrease λ
- High variance → More data, regularization, increase λ

Demonstrations:

Housing price polynomial fits (underfit/just right/overfit)
Classification overfitting examples
Regularization path visualization
Cross-validation for λ selection
Feature selection with Lasso

Lecture 11: Neural Networks - Basics¶

File: 08_neural_networks_basics.ipynb

Topics:

Biological motivation
Perceptron and activation functions
Multi-layer perceptrons
Backpropagation algorithm
Gradient checking
Weight initialization
Mini-batch training

Implementations:

Neural network from scratch
Backpropagation step-by-step
MNIST digit classification
Activation function comparison
Learning curve analysis

Key Algorithms:

Forward propagation
Backward propagation
Parameter updates

Lecture 12: Neural Networks - Advanced¶

File: 09_neural_networks_advanced.ipynb

Topics:

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Regularization techniques (Dropout, Batch Norm)
Optimization algorithms (Adam, RMSprop)
Transfer learning
Practical tips and tricks

Projects:

Image classification with CNNs
Sequence modeling with RNNs
Fine-tuning pretrained models

Part II: Unsupervised Learning¶

Lecture 14: Clustering¶

File: 12_clustering.ipynb

Topics:

K-Means algorithm
Choosing K (elbow method, silhouette)
Hierarchical clustering
DBSCAN
Gaussian Mixture Models (GMM)
EM algorithm

Implementations:

K-Means from scratch
Hierarchical clustering (all linkages)
GMM with EM
Cluster validation metrics
Real applications (customer segmentation)

Lecture 15-17: Dimensionality Reduction¶

File: 13_dimensionality_reduction.ipynb

Topics:

Principal Component Analysis (PCA)
Eigenvalue decomposition
Singular Value Decomposition (SVD)
Choosing number of components
Independent Component Analysis (ICA)
Factor Analysis
Autoencoders

Applications:

Data visualization
Noise reduction
Feature extraction
Compression

Anomaly Detection¶

File: 14_anomaly_detection.ipynb

Topics:

Gaussian distribution
Anomaly detection algorithm
Multivariate Gaussian
Choosing threshold ε
Anomaly detection vs supervised learning
One-class SVM

Use Cases:

Fraud detection
Manufacturing defects
System monitoring

Part III: Learning Theory¶

Lecture 9: Learning Theory¶

File: 10_learning_theory.ipynb [ENHANCED!]
Source: Lecture 9 Transcript (Friday Section - Learning Theory)

Topics:

Core assumptions of learning theory (new!)
Bias and variance from parameter view (new!)
Sampling distributions and estimators
Empirical risk minimization (ERM)
VC dimension
PAC learning
Sample complexity
Uniform convergence

From the Lecture:

“This deepens your understanding of how machine learning works under the covers. What are the assumptions we’re making and why do things generalize.” - TA Anand

New Foundations:

Assumption 1: Data distribution D exists
- Training and test data from same distribution
- This is critical for generalization!
Assumption 2: Independent sampling (i.i.d.)

The Learning Process:

S (random variable) → Algorithm A (deterministic) → θ̂ (random variable)

“When you feed a random variable through a deterministic function, you get a random variable”

Bias-Variance: Parameter Space View:

Imagine running learning algorithm many times with different samples
Each run gives different θ̂ → cloud of points in parameter space
Bias: Is cloud centered on true θ*? (first moment)
Variance: How spread out is cloud? (second moment)

Four Algorithm Types:

Bias	Variance	Behavior
Low	Low	✓ Best: Centered, tight
Low	High	Centered but spread out
High	Low	Off-center but consistent
High	High	Worst: Off-center, spread out

Effects of Data Size m:

↑ m → ↓ Variance (more stable)
↑ m → Bias stays same (assumptions unchanged)

Effects of Regularization:

↑ λ → ↓ Variance (more constraints)
↑ λ → May increase bias (stronger assumptions)

Theoretical Results:

Hoeffding inequality
Union bound
Training/test error relationship
Generalization bounds

Lecture 10: Decision Trees and Ensembles¶

File: 07_decision_trees.ipynb [NEW!]
Source: Lecture 10 Transcript (Decision Trees, Bagging, Boosting)

Topics:

Decision trees from scratch (new!)
Recursive partitioning
Split functions and loss functions (new!)
Why cross-entropy beats misclassification loss
Gini impurity
Tree depth and overfitting
Ensemble methods (new!)
Bagging and Random Forests
Boosting (AdaBoost, Gradient Boosting)

From the Lecture:

“Decision trees are one of our first examples of a non-linear model” - TA Raphael Townshend

The Skiing Example:

Problem: Predict if you can ski given month and latitude
Data: Northern Hemisphere winter (Jan-Mar), Southern Hemisphere winter (Jun-Aug)
Challenge: Non-linearly separable regions
Solution: Recursive rectangular partitions

“The tree is basically gonna play 20 Questions with this space”

Greedy, Top-Down, Recursive Partitioning:

Start with overall space
Ask best question: “Is latitude > 30°?” or “Is month < 3?”
Split space into two regions
Recursively apply to each region
Stop when pure or max depth

Split Function: S_p(j, t)

R₁ = {x ∈ R_p : x_j < t}
R₂ = {x ∈ R_p : x_j ≥ t}

j = feature index
t = threshold value

Loss Functions Comparison:

Misclassification Loss (Don’t Use!):

L = 1 - max_c(p̂_c)

Problem from lecture: Can’t distinguish between splits!

Parent: 900 pos, 100 neg → Loss = 100
Split 1: (700,100) + (200,0) → Loss = 100  
Split 2: (400,100) + (500,0) → Loss = 100  (clearly better but same loss!)

Cross-Entropy Loss (Use This!):

L = -Σ p̂_c log(p̂_c)

“From information theory: Number of bits needed to communicate which class”

Gini Impurity (Also Good):

L = 1 - Σ p̂_c²

Implementations:

Skiing classifier (lecture example recreated!)
Decision tree visualization (20 Questions)
Loss function comparison (verifying lecture claim)
Tree depth experiments (2, 4, 8, unlimited)
Overfitting demonstration
Bootstrap aggregating (bagging)
Random Forest
AdaBoost and Gradient Boosting

Key Insights:

Advantages: Interpretable, handles non-linearity, no scaling needed
Disadvantage: High variance (overfits easily)
Solution: Ensemble methods reduce variance!

When to Use:

✓ Need interpretability
✓ Mixed data types
✓ Non-linear patterns
✗ Need stable predictions → Use Random Forest instead

Lecture 13: ML Strategy¶

File: 11_ml_strategy.ipynb

Topics:

Orthogonalization
Single number evaluation metric
Train/dev/test distributions
Human-level performance
Error analysis
Bias and variance with mismatched data
Transfer learning
Multi-task learning
End-to-end deep learning

Practical Advice:

Debugging learning algorithms
Getting more data
Feature engineering vs deep learning

Part IV: Special Topics¶

Recommender Systems¶

File: 15_recommender_systems.ipynb

Topics:

Content-based filtering
Collaborative filtering
Matrix factorization
Deep learning for recommendations
Evaluation metrics

Implementation:

Movie recommendation system
Item-item collaborative filtering
Neural collaborative filtering

Lecture 18-20: Reinforcement Learning¶

File: 16_reinforcement_learning.ipynb

Topics:

Markov Decision Processes
Value iteration
Policy iteration
Q-Learning
Deep Q-Networks (DQN)
Policy gradients

Examples:

GridWorld
CartPole
Atari games (conceptual)

🚀 Getting Started¶

Prerequisites¶

Python: 3.8+

Required Libraries:

pip install numpy pandas matplotlib seaborn scikit-learn scipy tensorflow torch

Or install from requirements:

pip install -r requirements.txt

Installation¶

# Clone repository
git clone https://github.com/PavanMudigonda/aiml.git
cd aiml/2-maths/cs229-course

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

Quick Start¶

# Start with Lecture 1
jupyter notebook 01_linear_regression.ipynb

📖 Learning Path¶

Beginner Track (Weeks 1-4)¶

Focus on supervised learning fundamentals:

Lecture 1: Linear Regression
Lecture 2: Logistic Regression
Lecture 3: Regularization
Lecture 4: Generative Models

Time: 4 weeks (10-15 hours/week)

Intermediate Track (Weeks 5-8)¶

Advanced supervised learning: 5. Lecture 5: Support Vector Machines 6. Lecture 6: Neural Networks (Basics) 7. Lecture 7: Neural Networks (Advanced) 8. Lecture 8: Clustering

Time: 4 weeks (12-18 hours/week)

Advanced Track (Weeks 9-12)¶

Unsupervised learning and theory: 9. Lecture 9: Dimensionality Reduction 10. Lecture 10: Anomaly Detection 11. Lecture 11: Learning Theory 12. Lecture 12: ML Strategy

Time: 4 weeks (10-15 hours/week)

Specialized Topics (Weeks 13-14)¶

Lecture 13: Recommender Systems
Lecture 14: Reinforcement Learning

Time: 2 weeks (8-12 hours/week)

Total Duration: 14 weeks for comprehensive mastery

🎯 How to Use¶

For Self-Study¶

Watch CS229 lecture videos (available on YouTube)
Read corresponding lecture notes
Work through notebook with code examples
Complete practice exercises
Implement algorithms from scratch
Apply to real datasets

For Coursework¶

Use as lab assignments
Code walkthroughs in recitation
Project templates
Exam preparation

For Reference¶

Algorithm implementations
Mathematical derivations
Debugging templates
Best practices

📊 Datasets Used¶

Dataset	Lectures	Description
California Housing	1, 3	Regression, 8 features, 20k samples
Breast Cancer	2, 5	Binary classification, 30 features
MNIST Digits	2, 6, 7	Image classification, 28×28 pixels
Iris	2, 4, 8	Multi-class, 4 features, 150 samples
20 Newsgroups	4	Text classification
MovieLens	13	Recommender systems
Synthetic	Multiple	Generated for demonstrations

🔑 Key Concepts Reference¶

Supervised Learning¶

Linear Models:

# Linear Regression
h(x) = θᵀx
J(θ) = (1/2m)Σ(h(x⁽ⁱ⁾) - y⁽ⁱ⁾)²

# Logistic Regression  
h(x) = σ(θᵀx) where σ(z) = 1/(1+e⁻ᶻ)
J(θ) = -(1/m)Σ[y log(h(x)) + (1-y)log(1-h(x))]

Regularization:

# Ridge (L2)
J(θ) = MSE + λΣθⱼ²

# Lasso (L1)
J(θ) = MSE + λΣ|θⱼ|

Neural Networks:

# Forward pass
aˡ = σ(Wˡaˡ⁻¹ + bˡ)

# Backward pass
δˡ = (Wˡ⁺¹)ᵀδˡ⁺¹ ⊙ σ'(zˡ)

Unsupervised Learning¶

K-Means:

Initialize centroids randomly
Assign points to nearest centroid
Update centroids as mean of assigned points
Repeat until convergence

PCA:

Standardize data: X' = (X - μ)/σ
Compute covariance: Σ = (1/m)XᵀX
Eigendecomposition: Σ = UΛUᵀ
Project: X_reduced = XU_k

💡 Best Practices¶

Code Quality¶

✅ Vectorize operations (avoid loops)
✅ Document functions with docstrings
✅ Use meaningful variable names
✅ Add type hints
✅ Write unit tests

Model Development¶

✅ Always split train/dev/test
✅ Start simple, increase complexity
✅ Visualize data before modeling
✅ Monitor training curves
✅ Perform error analysis
✅ Compare multiple baselines

Debugging¶

When model doesn’t work:

Check data: Visualize, check statistics
Check implementation: Gradient checking
Check hyperparameters: Learning rate, regularization
Check convergence: Plot cost function
Check for bugs: Unit tests, assertions

📝 Practice Problems¶

Each lecture includes:

5-8 in-lecture exercises: Integrated with material
8-10 practice problems: End of notebook
1-2 projects: Apply to real datasets

Additional Resources¶

See CS229_PRACTICE.md for:

140+ additional exercises
10 comprehensive projects
5 challenge problems
Solutions and hints

🏆 Projects¶

Project 1: Housing Price Prediction¶

Dataset: Boston/California Housing
Goal: Predict prices with < 10% error
Techniques: Linear regression, regularization, feature engineering

Project 2: Spam Detection¶

Dataset: SMS/Email spam
Goal: Classify with > 95% accuracy
Techniques: Naive Bayes, logistic regression, feature extraction

Project 3: Handwritten Digit Recognition¶

Dataset: MNIST
Goal: Achieve > 98% test accuracy
Techniques: Neural networks, CNNs

Project 4: Customer Segmentation¶

Dataset: E-commerce data
Goal: Identify meaningful customer groups
Techniques: K-Means, GMM, PCA

Project 5: Movie Recommender¶

Dataset: MovieLens
Goal: Personalized recommendations
Techniques: Collaborative filtering, matrix factorization

🤝 Contributing¶

Contributions welcome! Areas:

Additional examples
More exercises
Bug fixes
Performance improvements
Documentation enhancements

📚 References¶

Course Materials¶

CS229 Lecture Notes: Stanford CS229
Video Lectures: YouTube Playlist
Andrew Ng: Coursera Machine Learning

Books¶

Pattern Recognition and Machine Learning: Bishop
The Elements of Statistical Learning: Hastie, Tibshirani, Friedman
Deep Learning: Goodfellow, Bengio, Courville
Reinforcement Learning: Sutton and Barto

Online Resources¶

🎓 Learning Outcomes¶

After completing this course, you will:

✅ Understand fundamental ML algorithms deeply
✅ Implement algorithms from scratch
✅ Apply ML to real-world problems
✅ Debug and improve ML systems
✅ Choose appropriate algorithms for tasks
✅ Understand theoretical foundations
✅ Follow ML best practices
✅ Build end-to-end ML pipelines

⚖️ License¶

MIT License - Free for educational and commercial use

📧 Contact¶

Repository: github.com/PavanMudigonda/aiml
Issues: Report bugs via GitHub Issues

🙏 Acknowledgments¶

Andrew Ng and Stanford CS229 teaching staff
scikit-learn, TensorFlow, and PyTorch communities
All contributors to this repository

Start Learning Today! 🚀

“Machine learning is the science of getting computers to learn without being explicitly programmed.” - Arthur Samuel

CS229: Machine Learning Course (Stanford University)¶

📚 Course Overview¶

What You’ll Learn¶

🗂️ Lecture Structure¶

Part I: Supervised Learning¶

Lecture 1: Linear Regression¶

Lecture 3: Locally Weighted Regression¶

Lecture 2 & 4: Logistic Regression (Classification)¶

Lecture 8: Regularization and Bias-Variance¶

Lecture 5-6: Generative Learning Algorithms¶

Lecture 6-7: Support Vector Machines¶

Lecture 8: Regularization and Bias-Variance¶

Lecture 11: Neural Networks - Basics¶

Lecture 12: Neural Networks - Advanced¶

Part II: Unsupervised Learning¶

Lecture 14: Clustering¶

Lecture 15-17: Dimensionality Reduction¶

Anomaly Detection¶

Part III: Learning Theory¶

Lecture 9: Learning Theory¶

Lecture 10: Decision Trees and Ensembles¶

Lecture 13: ML Strategy¶

Part IV: Special Topics¶

Recommender Systems¶

Lecture 18-20: Reinforcement Learning¶

🚀 Getting Started¶

Prerequisites¶

Installation¶

Quick Start¶

📖 Learning Path¶

Beginner Track (Weeks 1-4)¶

Intermediate Track (Weeks 5-8)¶

Advanced Track (Weeks 9-12)¶

Specialized Topics (Weeks 13-14)¶

🎯 How to Use¶

For Self-Study¶

For Coursework¶

For Reference¶

📊 Datasets Used¶

🔑 Key Concepts Reference¶

Supervised Learning¶

Unsupervised Learning¶

💡 Best Practices¶

Code Quality¶

Model Development¶

Debugging¶

📝 Practice Problems¶

Additional Resources¶

🏆 Projects¶

Project 1: Housing Price Prediction¶

Project 2: Spam Detection¶

Project 3: Handwritten Digit Recognition¶

Project 4: Customer Segmentation¶

Project 5: Movie Recommender¶

🤝 Contributing¶

📚 References¶

Course Materials¶

Books¶

Online Resources¶

📈 Progress Tracker¶

Core Lectures (14 total)¶

Practice¶

🎓 Learning Outcomes¶

⚖️ License¶

📧 Contact¶

🙏 Acknowledgments¶