# 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 : CSE
• Pre-requisites : Logical knowledge

### 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.

# GET COURSE COUNSELLING TODAY

Get a 1-on-1 demo to understand what is included in the course and how it can benefit you from an experienced sales consultant. The demo session will help you enroll in this course with a clear vision and confidence.

### COURSE SYLLABUS

#### 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

#### 4Probability

• 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

#### 12Python

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

### Projects Overview

logistic regression

Highlights

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)

Highlights

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

### WHO IS THIS COURSE FOR ?

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

## SOFTWARE COVERED

##### Python

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.

# Flexible Course Fees

Choose the plan that’s right for you

Basic

2 Months Access

# \$92.89

Per month for 3 months

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

# \$199.05

Per month for 3 months

• 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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### Certification

• Top 5% of the class will get a merit certificate
• Course completion certificates will be provided to all students
• Build a professional portfolio
• E-verified profile that can be shared on LinkedIn

### SKILL LYNC WORKS TO GET YOU A JOB

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#### 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.