| Programming Fundamentals |
Python Basics (Loops, Functions, etc.) |
Practice |
| NumPy and Pandas for Data Handling |
Practice |
| Matplotlib and Seaborn for Visualization |
Practice |
| Scikit-learn for ML |
Practice |
| PyTorch Basics |
Practice |
| Tensor (multi-dimensional array) Manipulation |
Practice |
| Reproducibility Basics (seed, devices, inference) |
Practice |
| Training Models on CPU and GPU |
Practice |
| Weights and Biases (experiment tracking) |
Practice |
| Supervised Learning |
Linear Regression |
Both |
| Logistic Regression |
Both |
| K-Nearest Neighbors (K-NN) |
Both |
| Decision Trees |
Both |
| Random Forests |
Practice |
| Gradient Boosting (e.g., XGBoost) |
Practice |
| Support Vector Machines (SVM) |
Both |
| Unsupervised Learning |
K-Means Clustering |
Both |
| Principal Component Analysis (PCA) |
Both |
| t-SNE, MAP, Other Dimensionality Reduction Methods |
Practice |
| DBSCAN Clustering |
Practice |
| Hierarchical Clustering |
Practice |
| Evaluation |
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, etc.) |
Both |
| Underfitting, Overfitting |
Theory |
| Hyperparameter Tuning |
Practice |
| Cross-Validation |
Practice |
| Confusion Matrix and ROC Curve |
Both |
