- Assignment 1: Basic Python: Data types, Data structure and libraries like numpy,pandas,matplotlib,seaborn
- Assignment 2: Feature extraction and Data Augmentation of data.
- Assignment 3: ML pipeline by extracting features, training and testing. Data augmentation affects accuracy.
- Assignment 4: Transforming data using linear algebra.
- Assignment 5: Interpreting and Visualizing Data using Python libraries.
- Assignment 6: Principal Components Analysis (PCA).
- Assignment 7: Non-linear dimensionality reduction methods - ISOMAP.
- Assignment 8: t-Distributed Stochastic Neighbor Embedding (t-SNE).
- Assignment 9: Distance metrics and Introduction to KNN.
- Assignment 10: Implementing KNN from scratch.
- Assignment 11: Introduction to text processing and using KNN for Text Classification.
- Assignment 12: Introduction to Cross-Validation and Standardization techniques.
- Assignment 13: Perceptron and Gradient Descent.
- Assignment 14: Introduction to Gradient Descent
- Assignment 15: Types of Gradient Descent(Batch Gradient Descent, Stochastic Gradient Descent and Mini Batch Gradient Descent).
- Assignment 16: Support Vector Machine(SVM).
- Assignment 17: Introduction to Decision Trees.
- Assignment 18: Information Metrics and generalizability of Decision Trees.
- Assignment 19: Ensemble Methods and Random Forests.