Course Content

1. Introduction to Machine Learning
Understand what Machine Learning is and its real-world applications. Learn types of ML: Supervised, Unsupervised, and Reinforcement Learning.

2. Python for Machine Learning
Overview of NumPy, Pandas, and Matplotlib. Work with data manipulation and visualization.

3. Data Preprocessing
Handle missing values, encoding categorical data, feature scaling, and data cleaning techniques.

4. Exploratory Data Analysis (EDA)
Analyze datasets using statistical methods and visualization to discover patterns and insights.

5. Supervised Learning – Regression
Learn Linear Regression and evaluation metrics like MAE, MSE, and R² score.

6. Supervised Learning – Classification
Study Logistic Regression, KNN, Decision Trees, and Support Vector Machines.

7. Unsupervised Learning
Learn clustering techniques like K-Means and Hierarchical Clustering.

8. Model Evaluation & Validation
Understand train-test split, cross-validation, confusion matrix, accuracy, precision, recall, and F1-score.

9. Feature Engineering
Improve model performance by selecting and transforming important features.

10. Ensemble Learning
Learn Random Forest and basic boosting techniques to improve prediction accuracy.

11. Introduction to Deep Learning (Basics)
Understand Neural Networks and basic concepts of TensorFlow/Keras.

12. Model Deployment (Basic)
Deploy ML models using Flask or Streamlit. Convert trained models into usable web applications.

13. Real-World ML Project
Build an end-to-end project: data collection → preprocessing → model building → evaluation → deployment.