Tutors

Innomatics Research Labs

PROGRAM OVERVIEW

CURRICULUM

  Machine Learning - Supervised Learning Regression - Videos

  • Simple Linear Regression
  • Simple Linear Regression - 2
  • Mutiple Linear Regression
  • Multiple Linear Regression - 2
  • Multiple Linear Regression Lab
  • Polinomial regression
  • polynomial regression and Regularization Techniques
  • Regularization methods and logistic regression
  • Regularization and Polynomial Files
  • AdaBoost and Gradient Boosting Regression
  • Boosting Lab
  • Boosting Lab Files
  • Boosting Material

  Machine Learning - Supervised Learning Classification - Videos

  • Logistic Regression - 2
  • Logistic Regression Lab
  • Logistic Regression Lab - 2 Classification Metrics
  • Logistic Regression Lab - 3
  • Logistic Regression Files
  • Naive Bayes
  • Naive Bayes Lab
  • SMOTE_Oversampling_Undersampling
  • Naivebayes_Imbalance_class_files
  • Models with Class Imbalance Data
  • SVM
  • SVM
  • Decision Tree
  • Decision Tree II and Lab
  • Decision Tree Lab
  • Decision Tree Lab and K-NN Introduction
  • K-NN Lab Implimentation
  • Decision Tree Class Files
  • KNN Class Files
  • Ensemble Introduction
  • Random Forest Lab Class
  • Random Forest Lab File and Material
  • XGBoost
  • XGBoost Class Files
  • Ada-Boosting

  Machine Learning - Unsupervised Learning - Videos

  • K Means and Hierarchical Clustering
  • K Means and Hierarchical Clustering Material
  • K - Means Lab
  • K Means Lab Material
  • PCA
  • PCA in Python
  • Logistic Regression with PCA and Market Basket Analysis
  • Logistic_Regression_PCA files