Course Duration – 25 Hours
| Supervised Learning |
| Regression - Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Ridge and Lasso Regression
- Model and Performance Evaluation
| Classification - Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machine(SVM)
- Linear Discriminant Analysis(LDA)
- Kernel SVM
- Naïve Bayes
- Decision Tree
- Random Forest
- Model and Performance Evaluation
|
| Unsupervised Learning | Ensemble Techniques |
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Factor Analysis
| - Max Voting
- Averaging
- Weighted Average
- Stacking, Blending
| Bagging & Boosting AdaBoost Gradient Boosting XGBoost CatBoost and Light GBM |
| Association Rule Learning | Reinforcement Learning |
| - Intelligent Agents
- Markov Decision Processes
- Dynamic Programming
- Monte Carlo
- Approximation Methods
|
| Recommender Systems(RS) | Deployment Strategies |
- Popularity Based RS
- Content-Based Filtering
- Collaborative Filtering
- Hybrid Approaches
| - Model Pipelines and Applications
- Model Architecture
- API Development and CI/CD concepts
- Paas and AWS deployment
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