Implementation of machine learning algorithms from scratch
The following algorithms are implemented to date:
- Train Test Split
- Stratified Split
- K-Fold Cross Validation
- Stratified K-Fold Cross Validation
- Linear Regression with Batch Gradient Descent optimizer
- Linear Regression with Mini-Batch Gradient Descent optimizer
- Least Absolute Deviation (LAD) Regression
- Multiple Linear Regression
- Logistic Regression
- Multiple Logistic Regression
- K Nearest Neighbors
- K-Means Clustering
- Implementing Receiver Operating Characteristic (ROC) Curve
- Implementing Precision Recall Curve (PRC)
- Word2Vec implementation in Keras
- Word2Vec implementation in PyTorch
- Simulating Random Walks
- How random are Python numbers?
- Principal Component Analysis
- Singular Value Decomposition
- Collaborative Filtering
- Bag of Words (BOW) modelling
- Linear Discriminant Analysis (LDA) v/s Principal Component Analysis (PCA)
- Understanding Loss Functions
- Multi-Label Classification
- Recommendation Evaluation Metrics
- Hierarchical Clustering
- Density-based spatial clustering of applications with noise (DBSCAN)
- Naive Bayes Classifier