Machine Learning

 

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
  • Apriori
  • Eclat
  • 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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