15 Practical Laboratories
Hands-On Exercises to Master Machine Learning Concepts
Each Practical Includes:
- ✅ Detailed lab guide with objectives
- ✅ Jupyter notebook starter template
- ✅ Sample datasets
- ✅ Solution code with explanations
- ✅ Learning outcomes checklist
- ✅ Troubleshooting guides
Practicals by Phase
Phase 1: Foundations & Setup (Practicals 1-2)
Phase 2: Supervised Learning (Practicals 3-5)
Phase 3: Unsupervised Learning (Practicals 6-8)
Phase 4: Advanced Topics (Practicals 9-13)
Phase 5: Real-World Applications (Practicals 14-15)
Complete Practicals List
| Practical | Title | Unit | Duration | Key Skills |
|---|---|---|---|---|
| P1 | Python Foundations | Unit 1 | 3 hours | NumPy, Pandas, Matplotlib |
| P2 | Data Exploration | Unit 1 | 3 hours | EDA, Cleaning, Visualization |
| P3 | Linear Regression | Unit 2 | 4 hours | Regression, Evaluation |
| P4 | Logistic Regression | Unit 2 | 4 hours | Classification, ROC |
| P5 | Decision Trees | Unit 2 | 4 hours | Tree Models, Ensembles |
| P6 | K-Means Clustering | Unit 3 | 4 hours | Clustering, Segmentation |
| P7 | Hierarchical Clustering | Unit 3 | 3 hours | Dendrograms, Linkage |
| P8 | PCA Analysis | Unit 3 | 4 hours | Dimensionality, Feature Reduction |
| P9 | SVM Classification | Unit 4 | 4 hours | SVM, Kernels, Tuning |
| P10 | Neural Networks | Unit 4 | 5 hours | ANN, TensorFlow, Keras |
| P11 | Time Series | Unit 5 | 4 hours | ARIMA, Forecasting |
| P12 | Ensemble Methods | Unit 4 | 4 hours | Boosting, Bagging |
| P13 | XGBoost & Advanced | Unit 4 | 4 hours | XGBoost, Advanced Models |
| P14 | NLP Project | Unit 5 | 5 hours | Text Processing, NLP |
| P15 | Capstone Project | Unit 5 | 8 hours | Full ML Pipeline, Deployment |