Python for Machine Learning

Curriculum

1. Introduction to Python and Jupyter Notebooks

2. Numerical Data Processing with NumPy

3. Data Analysis with Pandas

4. Crafting Visualizations with Matplotlib

5. Introduction to Machine Learning Concepts

   – Understanding Supervised vs Unsupervised Learning

   – Types of Machine Learning – Classification vs Regression

   – Evaluation Techniques

6. Machine Learning Methods – Theory and Practice

   – Linear Regression

   – Logistic Regression

   – K Nearest Neighbors

   – Support Vector Machine

   – Decision Trees

7. Unsupervised Learning Methods

8. Feature Engineering and Data Preparation

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