Math behind Machine Learning & Artificial Intelligence using Python

Math behind Machine Learning & Artificial Intelligence using Python

A 3 month course which takes the student through all the math concepts that he/she requires to get into ML/AI domain

  • Domain : DATA SCIENCE
  • Pre-requisites : Logical knowledge
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A Quick Overview

In this course, you learn more about the basics of Math behind Machine Learning & Artificial Intelligence. This course is specially designed for Engineering students to understand the concept behind Machine Learning & Artificial Intelligence.


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1Basic concepts

  • Sets
  • Subsets
  • Power set
  • Venn Diagrams
  • Trigonometric functions
  • Straight lines
  • A.M, G.M and H.M
  • Concepts of Vectors

2Permutation & Combinations

  • Introduction & basics
  • Fundamental principle of counting
  • Permutations
  • Combinations

3Statistics - I

  • First business moment
  • Second business moment
  • Third business moment
  • Fourth business moment


  • Introduction
  • Random experiments
  • Conditional probability
  • Joint probability

5Statistics – II

  • Z Scores
  • Confidence interval
  • Correlation
  • Covariance

6Probability - II

  • Introduction
  • Uniform Distribution
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution

7Likelihood (for Logistic regression)

  • Introduction
  • Odds
  • Log odds
  • Maximum likelihood vs probability
  • Logistic regression

8Gradient descent (for Linear & Logistic regression)

  • Loss function
  • Cost function
  • Gradient descent for linear regression
  • Gradient descent for logistic regression

9Linear Algebra (for PCA)

  • Matrices
  • Types of matrices
  • Operation on matrices
  • Eigen values
  • Eigen vectors

10Derivatives (for Neural network)

  • Derivatives
  • Intuitive idea of derivatives
  • Increasing & decreasing function

11Backpropagation (for Deep learning)

  • Chain rule
  • Maxima & minima
  • Back propagation
  • Cost function for deep learning


  • Basics of Python
  • If else
  • For loop
  • Data types

Projects Overview

logistic regression


To perform a logistic regression algorithm taught in the t course with simple random sampling and stratified sampling and identify the differences in the result (Data will be given in the class)

Gradient descent


Perform gradient descent for ANN in Python (Data will be given in class)


  • Students in Mechanical, Aerospace, Electrical & Automobile Engineering
  • Freshers looking to gain project experience on Machine Learning & Artificial Intelligence



The software that will be used as a part of this course is Python and a compiler for Python, sublime text. Python finds widespread use in the industries, ranging from data analysis for simulations to automation where mundane tasks can be automated to save time.

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Our courses have been designed by industry experts to help students achieve their dream careers

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Flexible Course Fees

Choose the plan that’s right for you


2 Months Access


Per month for 3 months

  • Access Duration : 2 months
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Email Support : Available
  • Forum Support : Available

Lifetime Access


Per month for 3 months

  • Access Duration : Lifetime
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Individual Video Support : 12/month
  • Group Video Support : 12/month
  • Email Support : Available
  • Forum Support : Available
  • Telephone Support : Available
  • Dedicated Support Engineer : Available

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  • Course completion certificates will be provided to all students
  • Build a professional portfolio
  • Automatically link your technical projects
  • E-verified profile that can be shared on LinkedIn


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Frequently Asked Questions

1Who are the instructors and what is the learning process?

Our instructors are industry experts working in Fortune 500 companies. We partner with them to deliver the lectures online. You will be given access to recorded content and assignments each week.

2Are there any prerequisites for this course?

You should be pursuing or have graduated with a B.E/B.Tech in Mechanical, Aerospace, Electrical or Automotive Engineering.

3What kind of support I can expect? What if I have doubts?

Our support system is amazing!. You can read our reviews on Google to see this. We focus on one-on-one support which no one else does. We will communicate with you through videoconferencing, WhatsApp messages/calls, individual online sessions and also in person. Doubts and queries are addressed by a dedicated support engineer who is assigned to you to walk you through your problem areas and clarify any queries that you may have.

4How is this different from what I learnt in college?

Our courses are crafted after consultation with industry experts. This gives you the opportunity to apply what you have learned only as theory and work on projects that will give you a leg up in your career aspirations - be it an MS admit, a new job or growth within your organization. This course will help you bridge the gap between academia and industry and get you market-ready.

5What advantages will I gain by taking this course?

You will have an edge over your peers by working extensively on industry-relevant projects, practice on tools and software that will set you apart and help you in getting ahead of the competition. Our course will strengthen your portfolio to get better grants and scholarship opportunities for MS Admits, explore options in Research & Development, and land that much-coveted job in top core companies. 

6Do I get access to the software?

Python is an open source. You can download it for free.

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