Machine Learning Learning Path

Machine Learning Path 🤖

1. Machine Learning (coursera)

🟢 Foundations (Modules 1–4)

Module 1 – Introduction

• What is machine learning?
• Supervised vs unsupervised learning
• Basic concepts and terminology

Module 2 – Linear Regression (One Variable)

• Predicting continuous values
• Cost function
• Gradient descent

Module 3 – Linear Algebra Review (Optional)

• Vectors and matrices
• Needed for multi-variable models

Module 4 – Linear Regression (Multiple Variables)

• Feature engineering
• Vectorized implementation
• Normal equation
• Best practices

These modules build the mathematical and conceptual base.

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🟡 Core Supervised Learning (Modules 5–7)

Module 5 – Octave/MATLAB Tutorial

• Programming environment for assignments

Module 6 – Logistic Regression

• Classification problems
• Sigmoid function
• Decision boundaries
• Multi-class classification

Module 7 – Regularization

• Preventing overfitting
• Bias vs variance

This is where you move from regression to classification.

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🔵 Neural Networks (Modules 8–9)

Module 8 – Neural Networks: Representation

• Why neural networks?
• Forward propagation
• Hidden layers

Module 9 – Neural Networks: Learning

• Backpropagation
• Training neural networks
• Digit recognition example

This introduces deep learning concepts at a foundational level.

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🟣 Model Evaluation & System Design (Modules 10–11)

Module 10 – Advice for Applying Machine Learning

• Debugging learning algorithms
• Error analysis
• Learning curves

Module 11 – Machine Learning System Design

• Building real-world systems
• Skewed data
• Precision/recall

Focus shifts from algorithms to practical decision-making.

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🟤 Advanced Supervised Learning

Module 12 – Support Vector Machines

• Large-margin classifiers
• Kernels

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🟢 Unsupervised Learning

Module 13 – Unsupervised Learning

• K-means clustering

Module 14 – Dimensionality Reduction

• Principal Component Analysis (PCA)

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🟡 Special Topics

Module 15 – Anomaly Detection

• Gaussian distribution
• Outlier detection

Module 16 – Recommender Systems

• Collaborative filtering
• Matrix factorization

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🔵 Large Scale & Applications

Module 17 – Large Scale Machine Learning

• Handling big datasets
• Stochastic gradient descent

Module 18 – Application: Photo OCR

• End-to-end system design
• Real-world pipeline

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Suggested Study Order

If studying seriously:

1. Modules 1–4 (foundation)
2. Modules 6–7 (classification + regularization)
3. Modules 8–9 (neural networks)
4. Modules 10–11 (practical ML)
5. Remaining modules based on interest

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2. Machine Learning Specialization

Alternative: Machine Learning Specialization(Coursera)

1. Supervised Machine Learning: Regression and Classification

• Week 1: Introduction to Machine Learning
• Week 2: Regression with multiple input variables
• Week 3: Classification

2. Advanced Learning Algorithms

• Week 1: Neural Networks
• Week 2: Neural network training
• Week 3: Advice for applying machine learning
• Week 4: Decision trees

3. Unsupervised Learning, Recommenders, Reinforcement Learning

• Week 1: Unsupervised learning
• Week 2: Recommender systems
• Week 3: Reinforcement learning

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Related Posts

• Stanford AI Scientist Roadmap 2026 — the broader career roadmap this learning path fits into
• Machine Learning: Introduction and Core Algorithms — where the path actually begins