This repository contains all the topics, resources, and links to supplemental material covered during the course. Below is an organized outline of the course content:
All topics link -> https://docs.google.com/document/d/1FBbFvc9AGPnU6Sx8pNTswVIAep7HVr6ruJETsHyDqN8/edit?usp=sharing
- Association Rule Mining
- Decision Trees
- Ensemble Methods
- Random Forest
- K-Nearest Neighbors (KNN)
- Bayesian Learning
- Feature Scaling
- Adaboosting and SEMME
- Clustering
- Model Evaluation
- Regression
- Topics:
- Apriori Algorithm
- FP-Growth Algorithm (with Lift)
- Resources:
- Topics:
- Generalized Search Mining
- Resources:
- Topics:
- Decision Tree with Gini Index
- Entropy and Information Gain Calculation
- Resources:
- Topics:
- Voting Regressor (Soft and Hard Voting)
- Bagging
- Resources:
- Topics:
- Cross-validation Techniques
- K-Fold, Stratified K-Fold, Leave-One-Out Validation
- Topics:
- Distance Measures (Hamming, Weighted Similarity)
- Distance-Weighted Nearest Neighbor Algorithm
- Topics:
- Bayesian and Naive Bayes Classification
- Gaussian Naive Bayes, Laplace Smoothing
- Sentiment Analysis using Multinomial and Bernoulli Naive Bayes
- Resources:
- Topics:
- Techniques and Importance
- Resources:
- Resources:
- Topics:
- K-Means and K-Means++
- Hierarchical Clustering (Agglomerative)
- OPTICS and DBSCAN
- Resources:
- Topics:
- Confusion Matrix and Classification Report
- Resources:
- Topics:
- Linear Regression
- Decision Tree Regression
Contributions and improvements are always welcome.