Machine Learning Syllabus

1. Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning
• Applications of Machine Learning
• Tools and Libraries

2. Getting Started

• Setting up the Environment
• Understanding Datasets
• Loading and Inspecting Data

3. Descriptive Statistics

• Mean, Mode, and Median
• Standard Deviation and Variance
• Percentiles

4. Data Distribution

• Normal Distribution
• Skewness and Kurtosis
• Outliers

5. Regression Analysis

• Linear Regression
• Polynomial Regression
• Multiple Regression

6. Data Preprocessing

• Scaling Data
• Splitting Data
• Categorical Data

7. Model Evaluation and Tuning

• Cross-Validation
• Grid Search
• Confusion Matrix

8. Classification Techniques

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees

9. Clustering

• K-Means Clustering