ML Using Python

ML Using Python

Machine Learning (ML) enables computers to learn from data. This syllabus focuses on ML fundamentals using Python, covering both theory and practical implementations.

What will you learn?

Week 1: Basics

• Introduction to ML and Python libraries (NumPy, pandas, matplotlib)
• Data preprocessing techniques
• Linear regression fundamentals
• Gradient descent optimization
• Performance evaluation (MSE, R2)

Week 2: Supervised Learning

• Logistic regression for classification
• Decision trees and random forests
• Support vector machines (SVM)
• Overfitting and regularization
• Cross-validation

Week 3: Unsupervised Learning and Advanced Topics

• Clustering (K-means, hierarchical)
• Dimensionality reduction (PCA)
• Basics of neural networks
• TensorFlow and Keras introduction
• Model evaluation and tuning

Week 4: Practical Applications

• Build ML models on real datasets
• Optimize and deploy ML models
• Mini-project: Predictive analytics or classification task
• Automate ML pipelines
• Debugging ML models