subjects.txt

1. Introduction to Machine Learning
1.1. Definition and Types of Machine Learning
1.2. Applications of Machine Learning
2. Basics of Python for Machine Learning
2.1. Python programming concepts
2.2. Introduction to libraries: Numpy, Pandas, Matplotlib
3. Supervised Learning
3.1. Concept of Supervised Learning
3.2. Regression and Classification
3.3. Evaluation of Supervised Learning Models
4. Unsupervised Learning
4.1. Concept of Unsupervised Learning
4.2. Clustering and Dimensionality Reduction
4.3. Evaluation of Unsupervised Learning Models
5. Reinforcement Learning
5.1. Basic Concepts of Reinforcement Learning
5.2. Q-learning and SARSA
6. Neural Networks and Deep Learning
6.1. Introduction to Neural Networks
6.2. Backpropagation and Neural Network Training
6.3. Introduction to Deep Learning
7. Convolutional Neural Networks
7.1. Concepts of Convolutional Neural Networks
7.2. Applications of Convolutional Neural Networks
8. Recurrent Neural Networks
8.1. Basics of Recurrent Neural Networks
8.2. Applications of Recurrent Neural Networks
9. Natural Language Processing
9.1. Basics of Natural Language Processing
9.2. Text Vectorization and Language Models
10. Support Vector Machines
10.1. Basic Concepts of Support Vector Machines
10.2. Applications of Support Vector Machines
11. Ensemble Learning
11.1. Bagging and Boosting
11.2. Random Forests
12. Feature Engineering and Selection
12.1. Techniques for Feature Engineering
12.2. Feature Selection Strategies
13. Model Evaluation and Validation
13.1. Cross-Validation
13.2. Overfitting, Underfitting and Model Selection
13.3. Metrics for Model Evaluation
14. Ethical Considerations in Machine Learning
14.1. Bias and Fairness
14.2. Privacy and Security
15. Trends in Machine Learning
15.1. Transfer Learning
15.2. Generative Adversarial Networks
15.3. Reinforcement Learning in Complex Environments