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