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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

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies Python

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

  • Extension of machine learning course
  • Understanding of Mathematics behind different machine learning algorithms such as linear regression, principal component analysis etc
  • This course is very important if someone want to learn deep learning
  • This course also involves lot of Mathematics with pen and paper

Course Syllabus

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week - 01 Basic concepts

  • In this session, we will learn about
    • Sets
    • Subsets
    • Power set
    • Venn Diagrams
    • Trigonometric Functions
    • Straight lines
    • A.M, G.M, and H.M
    • Concepts of Vectors

Week - 02 Permutation & Combinations

  • In this week's session, we will learn about:
    • Introduction to permutation and combinations
    • Basics of permutation and combinations
    • Fundamental principle of counting
    • Permutations
    • Combinations 

Week - 03 Statistics - I

  • In this week's session, we will learn about:
    • First business moment
    • Second business moment
    • Third business moment
    • Fourth business moment

Week - 04 Probability

  • In this week's session, we will learn about:
    • Introduction to probability
    • Random experiments
    • Conditional probability
    • Joint probability
 

Week - 05 Statistics – II

  • In this week's session, we will learn about:
    • Z Scores
    • Confidence interval
    • Correlation
    • Covariance
 

Week - 06 Probability - II

  • In this week's session, we will learn about:
    • Introduction to probability - II
    • Uniform Distribution
    • Normal Distribution
    • Binomial Distribution
    • Poisson Distribution
 

Week - 07 Likelihood (for Logistic regression)

  • In this week's session, we will learn about:
    • Introduction to likelihood statistics
    • Odds
    • Log odds
    • Maximum likelihood vs probability
    • Logistic regression

 

Week - 08 Gradient descent (for Linear & Logistic regression)

  • In this week's session, we will learn about:
    • Loss function
    • Cost function
    • The gradient descent for linear regression
    • The gradient descent for logistic regression
 

Week - 09 Linear Algebra (for PCA)

  • In this week's session, we will learn about:
    • Matrices
    • Types of matrices
    • Operation on matrices
    • Eigen values
    • Eigen vectors
 

Week - 10 Derivatives (for Neural network)

  • In this week's session, we will learn about:
    • Derivatives
    • Intuitive ideas of derivatives
    • Increasing & decreasing function

Week - 11 Backpropagation (for Deep learning)

  • In this week's session, we will learn about:
    • Chain rule
    • Maxima & minima
    • Back propagation
    • The cost function for deep learning

Week - 12 Python

  • In this week's session, we will learn about:
    • Basics of Python
    • If else
    • For loop
    • Data types
 

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

Industry Projects

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Logistic regression

In this project, the students are expected to perform the "Logistic Regression Algorithm" taught in the course based on the given input data.

Gradient descent

In this project, the students are expected to perform the gradient descent for ANN in Python based on the given input data.

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

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Be an expert in algorithms with our Math behind Machine Learning & Artificial Intelligence using Python course.

The primary goal of this Machine Learning crash course is to teach you all the essential math concepts used in Machine Learning & Artificial Intelligence algorithms. The Machine Learning & Artificial Intelligence field has seen a surge in this 21st century because of innovations in AI-based applications. Along with the coding knowledge, a basic understanding of mathematics will help you get better placements in this field.

Machine Learning & Artificial Intelligence algorithms are built using math concepts like Linear Algebra, Statistics and Probability. Also, fundamental math knowledge would enhance your analytical skill, which is significant for your career growth.

Skill-Lync’s Math behind Machine Learning & Artificial Intelligence using Python is a 24-week course. The fee structure for this Machine Learning online course is flexible. You can choose a plan from INR 7,000 to INR 15,000 as per your requirements.

Who Should Take This Course?

Industry experts designed our Math behind Machine Learning & Artificial Intelligence using Python course for students from Computer Science and Information Technology stream. You can take up this course if you are interested in developing simple Machine Learning or Artificial Intelligence-based applications.

If you aspire to become a professional analyst, then this Machine Learning certification course would help you be competent. Graduates looking forward to becoming an Artificial Intelligence and Machine Language engineer can opt for this Machine Learning course. This would help you gain industry-relevant skills, opening up many new opportunities for your career.

What Will You Learn in This Course?

This 24-week course on Math behind Machine Learning & Artificial Intelligence using Python will cover all the basics of mathematics that you need to build algorithms for Machine Learning and Artificial Intelligence. This Machine Learning certification course starts with Sets, Venn diagrams, Trigonometric functions, Vectors and Permutations, and Combinations. In the third week, you will learn statistics.

As you progress, you will learn about the cost function you need to evaluate the algorithm model. The loss function will help you to predict a value which dynamic in nature. As we all know, the best Machine Learning course must cover linear algebra, which is a vital concept for Machine Learning online courses. The syllabus will cover linear algebra matrices, and you will understand basic operations on matrices.

Also, you will learn the basics of Python and how to use basic commands like if and for loop for iterating a sequence. These concepts will help you design and train Machine Learning & Artificial Intelligence systems.

Skills You Will Gain

  • You will gain skills in mathematics that are essential to designing an algorithm for ML & AI systems.
  • By working on real-time projects, you will become industry-ready.
  • You will learn about Python, one of the best programming languages for Machine Learning and Artificial Intelligence.

Key Highlights of the Program

  • The duration of the course is 24 weeks.
  • Besides the course completion certificate for all participants, the top 5% of learners get a merit certificate.
  • You will get Individual Video Support, Group Video Support, Email Support, and Forum Support to clear your queries and doubts.
  • Real-time industry-relevant projects will make your learning purposeful.

Career Opportunities after Taking This Course

Taking this course would open up a plethora of career opportunities for you. As Machine Learning and Artificial Intelligence are booming, there is more demand for skilled professionals in the near future. Some positions that you can work for include,

  • Machine Learning Engineer
  • AI Data Analyst
  • AI Engineer

FAQs on Math behind Machine Learning & Artificial Intelligence using Python

  1. Who can take up this Machine Learning crash course?

Students and graduates with a technical background in Computer Science can opt for this course.

  1. Is this an online Machine Learning certification course?

Yes, this is a 100% online course.

  1. What is Machine Learning?

Machine Learning is part of AI where the inanimate objects are programmed to behave like human beings. They slowly learn, and with experience and training, their accuracy increases.

  1. What is the fee for this Machine Learning online course?

The fee structure is flexible, and you can choose a plan that suits you. The basic plan would give you two months of access, the pro plan would give you four months of access, and the premium plan would give you lifetime access.

  1. How much can a machine learning engineer earn?

According to Glassdoor, the average salary of a machine learning engineer is INR 8 LPA. After completing this course, you can also earn this average pay, and you can expect more once you become an experienced professional.

  1. Is there any certificate for completing this Math behind Machine Learning & Artificial Intelligence using Python course?

Yes, After completing this best Machine Learning course, you shall be given a course completion certificate. The top 5% of the scorers will be given a merit certificate alongside the course completion certificate.

  1. Is there any technical support available for this Machine Learning course students?

Yes, you can clear your queries with email and forum support.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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1 industry expert

Our instructors are industry experts along with a passion to teach.

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8 years in the experience range

Instructors with 8 years extensive industry experience.

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Areas of expertise

  • Machine Learning
  • Deep Learning

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