Machine Learning with Python and R

Duration of       Hours 


Duration time may vary depends on course progress


Machine Learning with Python and R

Machine Learning (ML) is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do.

In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.

Python is a popular object-oriented programming language having the capabilities of high-level programming language. Its easy to learn syntax and portability capability makes it popular these days. The followings facts gives us the introduction to Python −

Python was developed by Guido van Rossum at Stitching Mathematics Centrum in the Netherlands.

It was written as the successor of programming language named ‘ABC’.

It’s first version was released in 1991.

The name Python was picked by Guido van Rossum from a TV show named Monty Python’s Flying Circus.

It is an open source programming language which means that we can freely download it and use it to develop programs. It can be downloaded from

Python programming language is having the features of Java and C both. It is having the elegant ‘C’ code and on the other hand, it is having classes and objects like Java for object-oriented programming.

It is an interpreted language, which means the source code of Python program would be first converted into bytecode and then executed by Python virtual machine.



Course Content

Introduction to Machine Learning:

  • What is ML?

  • Applications of ML

  • Why ML is the Future

  • Types of ML

  • Installing Python and Anaconda (MAC & Windows)

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

  • Multiple Linear Regression

  • Multiple Linear Regression in Python

  • Polynomial Regression

  • Polynomial Regression in Python

  • Support Vector Regression (SVR)

  • SVR in Python

  • Decision Tree Regression in Python

  • Random Forest Regression in Python


  • Logistic Regression in Python

  • 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

  • Hierarchical Clustering in Python

Association Rule Learning:

  • Association Rule Learning in Python

  • Apriori

Reinforcement Learning:

  • Upper Confidence Bound (UCB)

  • Thompson Sampling

Natural Language Processing:

  • Natural Language Processing in Python

Deep Learning:

  • Artificial Neural Networks in Python

  • Convolutional Neural Networks in Python

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