Big Data and Machine Learning

Let's Build a smart Future

  • Analytics using Excel

    In the course you’ll learn all the mathematical and analysis functions used in MS Excel right from using small string functions to writing complex macros that could run programs on real time datasets. It is one of the most powerful tool still in use even after the advent of some path breaking technologies and is still being used by companies in Finance, Medical, Industrial, transport and many other sectors.

    1. Basic Functions and scripts in Excel
    2. Introduction to some intermediate functions.
    3. Introduction to Analytics and how it is performed using Excel.
    4. Introduction to Macros
    5. Data Processing and Manipulation in Excel
    6. Basic Machine Learning Algorithms in Excel

    Capstone Project 1: Real time Forecasting Model in Excel.

  • R Programming

    In this course, you’ll go through one of the most sought after open source scientific scripting platform used by millions of analytical startups and bigwigs. R is an open source software which can be used for a wide range of problem solving right from data processing to complex machine learning and deep learning to even creating analytical apps and hosting it on web.

    1. Introduction to R
    2. Installation and Setup
    3. Basic Data Structures in R
    4. Introduction to Functions and Packages in R
    5. Importing and exporting datasets in R
    6. Implementing predefined functions and packages

  • R Shiny Development

    n this semi module, you’ll learn how to make your analytical solutions more appealing to any lay man and work more closely with the visualization part of the data. Shiny is a package inside R, which helps you to develop web based applications and host it free of cost* on shiny’s server.

    1. Introduction to Shiny
    2. Setting up the UI and server in shiny
    3. Developing small applications and hosting it locally
    4. Importing and exporting datasets in shiny dashboards
    5. Using packages like ggplot, networkD3 and many more to visualize graphs and plots
    6. setting up an external server and hosting the app

    Capstone Project 2: Create a dashboard which would import datasets real-time from an external setup server and run multiple ML algorithms to show the real-time statistics and visualizations.

  • Classical Machine Learning

    The most sought after topic of the decade, here it is! We’ll drive you through the complete process of how a machine learns/responds to the code and what exactly does Machine learning means. What is deep learning, what is an AI answering many such questions of yours we’ll also take up some real world case studies to help you understand the real world implementation of ML and AI in your lives.

    1. Introduction to Machine Learning
    2. Why should you learn ML
    3. Where are you using these technologies in your life
    4. Different types of algorithms in ML
    5. Supervised, Unsupervised and Reinforcement Learning
    6. Breakdown of each of the above categories
    7. Artificial Neural Networks, Regressions, Decision Trees
    8. K-means, clustering, KNN
    9. Introduction to Deep Learning
    10. Restricted Boltzmann Machines, Autoencoders, Deep Neural Networks
    11. Genetic Algorithms

    Capstone Project 3: Implement various machine learning algorithms to address problems related to the field of cognitive science and brain science. Work on EEG (Electroencephalography data) and build complex classifiers that would classify the stress level leading to suicides in current state of the world

  • Natural Language Processing

    In this module, you’ll learn how to implement machine learning or even simple data processing algorithms when it comes to dealing with human understandable languages. You’ll learn the mechanism behind how the computer makes sense of your code or the high level language that we use to write our code in

    1. Introduction to NLP
    2. What is Text Mining
    3. Data cleaning and processing in NLP
    4. Staging the data
    5. Word frequencies and word clouds
    6. Similarities and Clustering
    7. Web scraping and crawling

    Capstone Project 4: In this project you’ll implement a web scraper and scrape a website to import data which can be a political speech or any dataset and then work on making sense of it in various ways using different NLP techniques

  • Python

    In this module, you’ll be introduced to again one of the most famous and sought after scripting langauge i.e Python. It is the most used language for implementation of Machine Learning or Data Science or Web development or even networking. We’ll learn how can we use Python for solving various data science problems using machine learning

    1. Introduction to Python
    2. Data structures and functions in Python
    3. Machine learning in Python
    4. Introduction to Scikit Learn, Scipy and Numpy in Python
    5. Implementing Machine Learning
    6. Importing and exporting of datasets in Python
    7. MNIST datasets
    8. Pattern recognition using Python

    Capstone Project 5: Work on the MNIST dataset to develop a ML algorithm which could recognize handwritten digits with very high level of accuracy.

Vikramank Singh

Researcher | Data Scientist | Entrepreneur

A keen researcher and a prolific data scientist, Vikramank Singh has an exhaustive background and a vast experience in Data Analytics and Machine Learning, currently working as Software Engineer with the research team at Facebook.

Course Fee:

Rs. 15,000/-

Avail group concessions

Rs. 13,000/- per head for a group of 4

Q. Do I need to carry any ID proof for the visit?

A. Carry a govt-issued ID proof AND your college ID card.

Q. Is photography/videography permitted at the visit?

A. No, unless instructed otherwise.

Q. What is the cancellation/refund policy?

A. No refunds on cancellation/no-show at the event.

Q. Is there a dress code to be followed?

A. Wear comfortable semi-formals.

Q. What materials do I need to carry?

A. You need to carry a Laptop and a Pen Drive.

Q. What if I miss a Lecture?

A. Extra lectures will be conducted to make up for topics missed.


Project 1
Real time Forecasting using Excel

Project 2 & 3
Dive into Cognitive Science

Capstone Project 4
Applying NLP Techniques

Project 5
Character Recognition using MNIST Dataset