Machine Learning with Python

This program introduces foundational and practical machine learning concepts using Python. It focuses on building models, understanding algorithms, and applying machine learning techniques to solve real-world data problems. Participants will gain hands-on experience in developing predictive models and analyzing datasets using Python-based tools.

Introduction to Machine Learning

• What is Machine Learning and its types

• AI vs ML vs traditional programming

• Machine learning workflow overview

• Real-world ML applications

• Problem-solving with data

Python for Machine Learning

• Python basics for ML development

• Working with NumPy and Pandas

• Data preprocessing techniques

• Data visualization fundamentals

• Handling datasets effectively

Supervised Learning Models

• Regression and classification concepts

• Linear and logistic regression

• Decision trees and random forests

• Model training and evaluation

• Improving model accuracy

Unsupervised Learning Techniques

• Clustering methods (K-Means, etc.)

• Dimensionality reduction techniques

• Pattern recognition in data

• Anomaly detection basics

• Real-world clustering applications

Model Evaluation & Optimization

• Performance metrics and evaluation

• Overfitting and underfitting concepts

• Cross-validation techniques

• Feature engineering basics

• Improving ML model performance