Programme highlights

Placement assistance

All the students would get placement assistance and help in cracking interviews

Mock interview will be conducted those interested 

Duration : 2 MONTHS

Weekdays : 8 am to 10 am

Week ends: 

Saturday: 10 am to 12 Sunday: 1 pm to  3 pm


Training Material

Training Material will be provided to student module wise

Industry Experts as faculty

Instructors are having industry experience and would relate all the training to industry use case

Course Fees

Course Fee: Rs. 40,000 (Classroom)

                     Rs. 35,000 (Online live)

The fees can be paid in 3 installments

Certifications (Specialization)

Student can choose the following specialization for Industry knowledge and relevant projects.

Specialization : Banking |Retail | Fintech



R Programming Catch up

  •  R LanguageProgramming Basics
  • R languageData Types
  • Structures and conditional statements
  • Introduction to R Studio
  • Data filtering and selecting
  • Data Mugging techniques in R

Statistics Catch up

  •  Basic Statistics and Exploratory Analysis
  • Descriptive summary statistics with Numpy
  • Summarize continous and categorical data
  • Outlier analysis

Introduction To Machine Learning

  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks
  •  Supervised Machine Learning algorithms
  • K-Nearest Neighbors (KNN) concept and application
  • Naive Bayes concept and application
  • Logistic Regression concept and application
  • Classification Trees concept and application
  • Unsupervised Machine Learning algorithms
  • Clustering with K-means concept and application
  • Hierarchial Clustering concept and application

Data Preparation for Model development

  •  Advanced Data Mugging
  • Outlier Analysis
  • Treating for missing values
  • Normalization vs Standardization of data

Model Development

  •  Setting up the project with ML workflow.
  • Data Preprocessing and statistical exploration
  • Building , Training and evaluation of Machine Learning Model

Parameter Tuning and Live rojects

  •  10 Live projects on Banking
  • 20 Assignment
  • 2 Exam