Machine Learning Specialization — Stanford & DeepLearning.AI

My coursework and assignments for the Machine Learning Specialization offered by Stanford University and DeepLearning.AI on Coursera (instructed by Andrew Ng).

The specialization consists of three courses covering supervised learning, advanced neural network techniques, and unsupervised/reinforcement learning.

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Courses

Course 1 — Supervised Machine Learning: Regression and Classification

Week	Content	Notebooks
Week 1	Python & Jupyter basics, model representation, cost function, gradient descent	C1_W1_Lab01 – C1_W1_Lab04
Week 2	NumPy vectorization, multiple-variable regression, feature scaling, polynomial regression, scikit-learn (GD & normal equation)	C1_W2_Lab01 – C1_W2_Lab06
Week 3	Classification, sigmoid function, decision boundary, logistic loss, logistic cost function, gradient descent for logistic regression, scikit-learn, overfitting, regularization	C1_W3_Lab01 – C1_W3_Lab09

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Course 2 — Advanced Learning Algorithms

Week	Content	Notebooks
Week 1 – Labs	Neurons & layers (TensorFlow), coffee-roasting neural network (TF & NumPy)	C2_W1_Lab01 – C2_W1_Lab03
Week 1 – Assignment	Neural network for binary classification (handwritten digit recognition)	C2_W1_Assignment
Week 2 – Labs	Softmax activation, ReLU, multiclass classification (TF), backpropagation, derivatives	C2_W2_SoftMax, C2_W2_Relu, C2_W2_Multiclass_TF, C2_W2_Backprop, C2_W2_Derivatives
Week 2 – Assignment	Multiclass neural network (digit recognizer — 10 classes)	C2_W2_Assignment
Week 3 – Labs	Model evaluation & selection, diagnosing bias and variance	C2W3_Lab_01, C2W3_Lab_02
Week 3 – Assignment	Advice for applying machine learning (bias/variance, regularization, learning curves)	C2_W3_Assignment
Week 4 – Labs	Decision trees, tree ensembles (random forests & XGBoost)	C2_W4_Lab_01, C2_W4_Lab_02
Week 4 – Assignment	Decision tree implementation from scratch	C2_W4_Decision_Tree_with_Markdown

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Course 3 — Unsupervised Learning, Recommenders & Reinforcement Learning

Week	Content	Notebooks
Week 1 – Assignment 1	K-Means clustering (image compression)	C3_W1_KMeans_Assignment
Week 1 – Assignment 2	Anomaly detection	C3_W1_Anomaly_Detection
Week 2 – Lab	PCA & data visualization	C3_W2_Lab01_PCA_Visualization_Examples
Week 2 – Assignment 1	Collaborative filtering recommender system	C3_W2_Collaborative_RecSys_Assignment
Week 2 – Assignment 2	Deep learning–based recommender system (neural network)	C3_W2_RecSysNN_Assignment
Week 3 – Lab	State-action value function example	State-action value function example
Week 3 – Assignment	Deep Q-Learning — Lunar Lander	C3_W3_A1_Assignment

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Tech Stack

• Python 3
• TensorFlow / Keras
• NumPy, Pandas, Matplotlib
• scikit-learn
• Jupyter Notebooks

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Repository Structure

Machine-Learning-Specialization-Stanford_DeepLearning/
├── Supervised_Machine_Learning/
│   ├── Week1/          # Python basics, model representation, cost function, gradient descent
│   ├── Week2/          # Vectorization, multi-variable regression, feature scaling, sklearn
│   └── Week3/          # Logistic regression, classification, regularization, overfitting
├── Advanced_Learning_Algorithms/
│   ├── Week1_Labs/     # Neurons, layers, TF coffee-roasting demo
│   ├── Week1_Assignment/
│   ├── Week2_Labs/     # Softmax, ReLU, multiclass, backprop
│   ├── Week2_Assignment/
│   ├── Week3_Labs/     # Model evaluation, bias-variance
│   ├── Week3_Assignment/
│   ├── Week4_Labs/     # Decision trees, tree ensembles
│   └── Week4_Assignment/
└── Unsupervised_Learning_Recommenders_Reinforcement_Learning/
    ├── Week1_Assignment1/   # K-Means clustering
    ├── Week1_Assignment2/   # Anomaly detection
    ├── Week2_Lab/           # PCA visualization
    ├── Week2_Assignment1/   # Collaborative filtering
    ├── Week2_Assignment2/   # Neural-network recommender
    ├── Week3_Lab/           # State-action value function
    └── Week3_Assignment/    # Deep Q-Learning (Lunar Lander)