Python for Machine Learning — From Data to Predictions

Description

Description:
Build machine learning models using real-world data. Learn the core techniques behind supervised and unsupervised learning using Python libraries like scikit-learn, pandas, and NumPy. Perfect for anyone looking to break into ML.

Format:
Video lessons, hands-on projects, Jupyter notebooks

Duration:
7 hours (self-paced)

Level:
Intermediate

Modules:

1. Introduction to Machine Learning

2. Data Preprocessing with pandas & NumPy

3. Supervised Learning: Regression & Classification

4. Unsupervised Learning: Clustering & Dimensionality Reduction

5. Model Evaluation & Metrics

6. Hyperparameter Tuning & Feature Engineering

7. Model Deployment Basics

Features:
✓ Real-world datasets
✓ Guided ML projects
✓ Practice notebooks & quizzes
✓ Model evaluation cheat sheet