Machine Learning with R

Course Duration : 40 hrs
20,000

About Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.


Course Overview

This foundation course focuses on the following key areas:

  • Machine Learning A-Z using R
  • Regression, Classification & Clustering
  • Association Rule Mining
  • Dimensionality Reduction

What We Offer

Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Class Room Training With Flexible Timing
Customized/On-demand Training
Unlimited Access to Exclusive Study Materials On Cloud

Chapter-1: Machine Learning A-Z using R

1.1 Data Pre-Processing – Getting the dataset
1.2 Importing library
1.3 Missing data
1.4 Categorical data
1.5 Splitting the data set
1.6 Feature scaling
1.7 EDA in details

 

Chapter-2: Regression

2.1 Multiple Linear Regression – Data set and Business Problem, Theory, Backward Elimination.
2.2 Polynomial Regression
2.3 Support Vector Regression
2.4 Decision Tree/ Random Forest Regression
2.5 Evaluating Model Performance – r-sqr, Adj r-sqr, Interpretation of the model.

 

Chapter-3: Classification

3.1 Logistic Regression – Data set and Business Problem, Theory
3.2 K-Nearest Neighbors (K-NN)
3.3 Support Vector Machine
3.4 Kernel SVM
3.5 DT and Random Forest
3.6 Evaluating Model Performance – Confusion Matrix, CAP Curve, AUC,ROC

 

Chapter-4: Clustering

4.1 K-Means clustering
4.2 No of K
4.3 Theory
4.4 Quality of a cluster

 

Chapter-5: Association Rule Mining

5.1 Apriori
5.2 Eclat

 

Chapter-6: Dimensionality Reduction

6.1 Principle Component Analysis
6.2 Model Selection –
6.3 K-fold cross validation
6.4 XGBoost