Machine Learning

Lesson 1: Introduction to Machine Learning

• Definition and significance of ML
• Machine learning vs. traditional programming

Lesson 2: Data Preprocessing

•  Data Cleaning
• Handling missing data
• Outlier detection and treatment

Lesson 3:Types of Machine Learning

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning

Lesson 4: Supervised Learning

• Linear Regression
• Simple and multiple linear regression
• Model evaluation and interpretation
• Logistic regression
• Decision trees
• Random forests
• k-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)

Lesson 5: Unsupervised Learning

• Clustering
• K-Means clustering
• Hierarchical clustering
• Dimensionality Reduction

Lesson 6: Evaluation and Model Selection

• Performance Metrics
• Accuracy, precision, recall, and F1-score
• ROC and AUC
• Overfitting and Underfitting

Lesson 7: Course Review and Conclusion

• Summarize key concepts and skills learned
• Discuss the importance of continuous learning in the field.