Machine Learning with Python and R

Machine Learning & Analytics

Course Description

The Machine Learning Online Course gives the learner a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. The course covers linear regression, K Nearest Neighbors, Clustering, SVM and neural networks using Python and R.

4 Days

Upon completion of this course, a learner should be able to:

  • Understand Machine Learning
  • Carry out Data processing
  • Perform Regression using Python and R
  • Perform Classification using Python and R
  • Clustering using Python and R, etc…

Basic Knowledge in R and Python is mandatory. It would be beneficial if the learner has Hadoop skills too

Introduction to Machine Learning

  • What is Machine Learning?
  • Applications of Machine Learning
  • Why Machine Learning is the Future
  • Installing R and R Studio (MAC & Windows)
  • Installing Python and Anaconda (MAC & Windows)

Data Pre-processing

  • Data Preprocessing
  • Importing the Libraries
  • Importing the Dataset
  • For Python learners, summary of Object-oriented programming: classes & objects
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling


  • Simple Linear Regression
  • Dataset + Business Problem Description
  • Simple Linear Regression in Python
  • Simple Linear Regression in R
  • Multiple Linear Regression
  • Multiple Linear Regression in Python
  • Multiple Linear Regression in R
  • Polynomial Regression
  • Polynomial Regression in Python
  • Polynomial Regression in R
  • Support Vector Regression (SVR)
  • SVR in Python
  • SVR in R
  • Decision Tree Regression in Python
  • Decision Tree Regression in R
  • Random Forest Regression in Python
  • Random Forest Regression in R


  • Logistic Regression in Python and R
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Confusion Matrix
  • CAP Curve


  • K-Means Clustering in Python and R
  • Hierarchical Clustering in Python and R

Association Rule Learning

  • Association Rule Learning in Python and R
  • Apriori

Reinforcement Learning

  • Upper Confidence Bound (UCB)
  • Thompson Sampling

Natural Language Processing

  • Natural Language Processing in R
  • Natural Language Processing in Python

Deep Learning

  • Artificial Neural Networks in Python and R
  • Convolution Neural Networks in Python and R

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