Machine Learning Inception Program

A 4 Month Live Online and Mentorship Program for professionals to kickstart their journey in Machine Learning

Enroll Now

4 months

Duration of the course

24th August, 2019

Starting Date

Live + Recorded

Mode of Teaching

7:00 PM (IST)

Sat and Sun Timings

Trusted by professionals

working at global companies

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Why Most Professionals Struggle

to become a Data Scientist

Multi-Disciplinary Skills

Data Science is not just coding. You need to be good in skills like Statistics and Business acumen to be successful.

Lack of a mentor

An experienced Data Scientist who knows everything inside out and can guide you towards the right path.

Fast Evolving Domain

Breakthroughs are happening everyday and its hard to keep up with the newly changing trends.

How this program works

The 3 pillars of Inception

Weekend Live Sessions

4+ hours of Group live coaching every weekend (7:00 PM IST) where you can get your doubts solved instantly. Every session is also recorded and available to watch later.

QnA and Mentoring Sessions

1+ hour of dedicated group live session every week where we resolve your questions and groom you as a Data Scientist. Also priority chat support to resolve your doubts outside class hours.

Projects and Assesments

We will show you how to integrate Machine Learning and Software Engineering to make real tangible projects. And weekly quizzes and assignments will help you reach there.

How to enroll

and what is the cost

Monthly Installments Plan
$ 119 $ 95

Per Month. For 4 months

Enroll Now

Facing issues in payment?

  • No Credit card Required
  • Deployement of ML Projects
  • Exclusive Student Portal
  • Weekly Assesments
  • Individual Attention to each student
  • Priority doubts resolution
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What You Will Learn

over the course of 4 months

  • Introduction to Basic Programming

    Introduction to Python

    Statistics 101

    Intro to Linux Commands

    SQL for Analytics

    How to use Git

  • Introduction to Machine Learning

    What is Machine learning?

    Where to use Machine learning and How to use it efficiently?

    Dissecting Machine learning

    Getting Started with Supervised Learning

    Introduction to Unsupervised Learning

  • Regression

    The idea behind Regression

    Intuition behind the model

    Mathematical / Algorithmic working of the model

    Parameters to tune - How to control the model.

  • Regression Techniques

    Linear Regression

    Polynomial Regression

    Lasso Regression

    Ridge Regression

    SVM Based Regression

    Tree Based Regression Techniques

  • Evaluating your model

    Intro to Regression Model Evaluation

    RMSE and MAE

    R Squared

    Adjusted R Squared

  • Mini Project 1 based on Regression
  • Classification

    Idea Behind Classification

    Intuition behind the classifier

    Mathematical / Algorithmic working of the classifier

    Parameters to tune - How to control the classifier

  • Classification Techniques

    Logistic Regression

    Decision Trees

    K Nearest Neighbours


    Naive Bayes

    Tree Based Regression Techniques

  • Evaluating your classifier

    Intro to Classification Model Evaluation

    Confusion Matrix

    Precision and Recall

    F Score

    Gain and Lift Chart

    ROC AUC Curve

    Understanding the Precision and Recall Tradeoff

  • Project Work 2 based on Classification
  • Thinking Like a Data Scientist

    How to prepare your data for Modelling

    Data Feature Engineering

    Resampling Methods

  • Building Strong Models using Ensembling

    Introduction to ensembling and the Why behind it


    Random Forest



    Stochastic Gradient Boosting



  • Unsupervised Learning

    What is Unsupervised Learning?

    K Means Clustering

    Spectral Clustering

    Hierarchical Clustering

    Clustering Project

    Dimensionality Reduction



  • Project Work 3 based on Clustering
  • Capstone Project 1 with Deployment

    Work on NYC Taxi Dataset

    Model Deployment on AWS

    Exploratory Data Processing

    Feature Engineering

    Model Evaluation

    Model Refinement

  • Capstone Project 2 with Deployment

    Work on Stack Overflow Dataset

    Major Concepts of NLP

    Model Deployment on Google Cloud

    Data I/O on Spark Clusters

    Exploratory Data Processing

    Feature Engineering

    Model Evaluation

    Model Refinement

What projects you will build

and what data sets will be used

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    Auto tagging of the stack overflow questions using the stack overflow open dataset. Using Spark we will handle 200+ GB dataset and deploy on google cloud.

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    Ride Price optimisation based on NYC Taxi data and deployment of the full project on Google Cloud.

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    Using Bigmart's sales dataset predict product placement, inventory management, customized offers and product bundling

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    Use Classification to predict if the customer will claim medical insurance or not. If yes, what will be the amount.

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    Use clustering to handle evaluation form filled by students of a turkey university.

What's Special in these projects

which gives you an edge in the industry

Model Deployement on Cloud

The projects would be developed and deployed on AWS and Google Cloud Environments to help you understand how Machine Learning projects are implemented at an industrial scale.

Big Data Components

We would be exposing you practically to more than 200 GB of data using Spark Big Data Framework in our capstone project.

Natural Language processing

We would be undestanding the Complete practical aspects of NLP while implementing our Stack overflow capstone project.

The DatumGuy Advantage


Full Stack Machine learning Projects from experts

Work with Real world datasets

With data from NYC Taxi, Stack overflow and Kaggle, you are sure to work on dataset that you can proudly showcase in an interview.

Learn how to deploy your models

Now your ML models will not just be available on your system, we will also learn how to deploy them on cloud and work with other teams in your organizations.


Learn alongside everyone or Learn at your own pace

Recordings available of each live session

You have the opportunity to attend each session live or watch the recordings at your own pace.

Access to Exclusive Student Portal 

Exclusive Student portal where every live video recordings will be broken down into furthur topics and posted.


Continuous Support and Mentorship

Weekly lIve QnA and Mentorship Sessions

We will have regular weekly Live QnA sessions where you will be able to ask any doubt regarding previous sessions. Also Mentorship sessions provide you with the right direction to achieve your career goals.

After Class Support

Chat and Facebook group support is available after class if you face any doubts and need to get it resolved immediately.


Resume Building and Career services 

Resume and Online Profile Review

You will receive personal resume and LinkedIn profile review sessions to maximize your chances of getting the best opportunity.

I am Ready to enroll

and kickstart my Data Science journey

Enroll Me

What our students say

about our quality and support

Do I get a certificate

when I complete the whole course


Complete the full course with all assignments successfully to obtain this verified certificate from DatumGuy

Frequently Asked Questions

What if I miss a live session?

Though this is a live course, all sessions are recorded and forever available to watch at a later time. If you watch the session at a later time, you will be able to post your doubts in the facebook group at any time and our team will get your doubt resolved in no time.

What are the timings?

We have a global audience from countries like USA, UK, Egypt, Sri Lanka, India and many other countries in our course. To make sure the sessions happen at waking hours for our whole audience, the sessions starts in the evening IST, usually at 7PM and ends in 1.5 - 2 hours.

Is it a basic course?

It starts up from scratch and covers the basic concepts with prerequisites in the first weeks. Then the difficulty level builds up through the months to give you a strong foundation of Machine learning.


I still have questions

Talk to us