https://github.com/wingkwong/machine-learning-cookbook

Learning Machine Learning
https://github.com/wingkwong/machine-learning-cookbook

machine-learning machine-learning-algorithms machine-learning-cookbook

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Learning Machine Learning

• Host: GitHub
• URL: https://github.com/wingkwong/machine-learning-cookbook
• Owner: wingkwong
• Created: 2023-05-29T13:12:45.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-31T14:51:22.000Z (over 2 years ago)
• Last Synced: 2025-02-16T03:43:21.670Z (8 months ago)
• Topics: machine-learning, machine-learning-algorithms, machine-learning-cookbook
• Language: Jupyter Notebook
• Homepage:
• Size: 248 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine Learning Cookbook

Welcome to the Machine Learning Cookbook! This roadmap will guide you through the essential topics and concepts in machine learning, helping you build a strong foundation in this exciting field. Follow this roadmap to progress systematically and gain a comprehensive understanding of machine learning.

## Chapter 1: Introduction to Machine Learning

- [1.1 Introduction to Machine Learning](chapter-01/01-introduction-to-machine-learning)

- [1.2 Importance and Applications of Machine Learning](chapter-01/02-importance-and-applications-of-machine-learning)

- [1.3 Types of Machine Learning](chapter-01/03-types-of-machine-learning)

## Chapter 2: Understanding Core Concepts

- [2.1 Data Representation and Feature Engineering](chapter-02/01-data-representation-and-feature-engineering)

- [2.2 Training and Testing Data](chapter-02/02-training-and-testing-data)

- [2.3 Model Evaluation Metrics](chapter-02/03-model-evaluation-metrics)

- [2.4 Overfitting and Underfitting](chapter-02/04-overfitting-and-underfitting)

- [2.5 Bias-Variance Tradeoff](chapter-02/05-bias-variance-tradeoff)

## Chapter 3: Supervised Learning

- 3.1 Introduction to Supervised Learning

- 3.2 Linear Regression

- 3.3 Logistic Regression

- 3.4 Decision Trees and Random Forests

- 3.5 Support Vector Machines (SVM)

- 3.6 Naive Bayes Classifier

- 3.7 K-Nearest Neighbors (KNN)

## Chapter 4: Unsupervised Learning

- 4.1 Introduction to Unsupervised Learning

- 4.2 Clustering Algorithms

- 4.3 K-Means Clustering

- 4.4 Hierarchical Clustering

- 4.5 Dimensionality Reduction

- 4.6 Principal Component Analysis (PCA)

- 4.7 Anomaly Detection

## Chapter 5: Reinforcement Learning 

- 5.1 Introduction to Reinforcement Learning

- 5.2 Markov Decision Processes

- 5.3 Model-Free Reinforcement Learning

- 5.4 Deep Reinforcement Learning

- 5.5 Multi-Agent Reinforcement Learning

- 5.6 Applications of Reinforcement Learning

## Chapter 6: Evaluation and Validation Techniques

- 6.1 Cross-Validation

- 6.2 Train-Test Split

- 6.3 Bias and Variance in Model Evaluation

- 6.4 Hyperparameter Tuning

- 6.5 Model Selection and Evaluation Strategies

## Chapter 7: Feature Selection and Engineering

- 7.1 Importance of Feature Selection

- 7.2 Feature Selection Techniques

- 7.3 Feature Engineering and Transformation

- 7.4 Handling Missing Data

- 7.5 Handling Categorical Data

## Chapter 8: Introduction to Deep Learning

- 8.1 Basics of Neural Networks

- 8.2 Activation Functions

- 8.3 Feedforward Neural Networks

- 8.4 Convolutional Neural Networks (CNN)

- 8.5 Recurrent Neural Networks (RNN)

- 8.6 Introduction to Deep Learning Frameworks

## Chapter 9: Model Deployment and Ethics

- 9.1 Model Deployment Process

- 9.2 Ethical Considerations in Machine Learning

- 9.3 Fairness and Bias in Machine Learning

- 9.4 Privacy and Security in Machine Learning

- 9.5 Challenges and Future of Machine Learning

## Chapter 10: Case Studies and Practical Examples

- 10.1 Image Classification

- 10.2 Sentiment Analysis

- 10.3 Recommender Systems

- 10.4 Fraud Detection

- 10.5 Natural Language Processing (NLP)

## Chapter 11: Conclusion and Next Steps

- 11.1 Recap of Key Concepts

- 11.2 Resources for Further Learning

- 11.3 Advanced Machine Learning Topics

- 11.4 Real-World Applications and Opportunities

Follow this roadmap, complete the exercises, and engage in hands-on projects to solidify your understanding of machine learning. Remember, practice and experimentation are key to mastering this field. Good luck on your machine learning journey!