Linear Algebra, Statistics, Calculus, & Probability. Together these 4 branches of Maths are the four foundational pillars of everything in Data Science domain

**Classes** : 30 **Days** : 3 months **Duration** : Weekdays / Weekends

Mathematics is exceedingly useful for developing intelligent systems that can take decisions autonomously. For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science.

Data Science is about developing models that can automatically extract important information and patterns from data. But here, an important question arises: ** what is the magic behind ML, and the answer is Mathematics**. Mathematics is the core of designing ML algorithms that can automatically learn from data and make predictions. Therefore, it is very important to understand the Maths before going into the deep understanding of ML algorithms.

**Statistics and Probability** form the core of **Data Analytics**. They are widely used in the field of machine learning to analyze, visualize, interpret data and discover insights. To understand how each algorithm works, you need to know **linear algebra**. You might have to revisit high-school mathematics for anything to do with **Calculus**. Machine learning uses the concepts of calculus to formulate the functions that are used to train algorithms. Learning mathematics in Data Science is not about solving a maths problem, rather understanding the application of maths in DS algorithms and their working.

During the training, we shall expose the students to **Python** for executing and solving the Maths problems. It is imperative that you are hands on with Python.

This specialization aims to bridge the gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

**Trainer:**

A Maths and Data Science expert, having more than 25 years of International experience working in EU/USA/Australia

**Who this course is for:**

:- The target audience are Engineers or Managers who are interested in learning Data Science, Machine Learning, IoT, Big Data or Artificial Intelligence

:- Any practising Manager or Engineer who desires to build better and optimal Predictive Models for their organizations

:- UG college students with major in DS or ML

:- Anybody who wants to control what goes behind the 3 lines of ML models

:- Managers who have to use the outcome of ML models

:- Anybody who deals with Data Mining or Data Visualization

:- Technical and Non Technical members of any Data Science project

- You are willing to learn Advanced Mathematics

- You have studied at least 12 years of basic Maths

- You have the basic knowledge of Python Programming

- You have studied at least 12 years of basic Maths

- You have the basic knowledge of Python Programming

**1. Linear Algebra for Data Science**

:- Vectors and Matrix Properties

:- Matrix Transpose and Inverse

:- Determinants

:- Dot Product

:- Eigenvalues and Eigenvectors

:- Matrix Factorization

:- Principal Component Analysis

:- Orthogonalization & Orthonormalization

**2. Calculus for Data Science**

:- Differential and Integral Calculus

:- Limit, Continuity and Partial derivatives

:- Step, Sigmoid, Logit, and ReLU Function

:- Maxima and Minima of a Function

:- Product and Chain Rule

:- Directional Gradient

**3. Probability for Data Science**

:- Joint, Marginal, and Conditional Probability

:- Probability Distributions (Discrete, Continuous)

:- Density Estimation

:- Maximum Likelihood Estimation

:- Regression with Maximum Likelihood

:- Bayes Theorem

**4. Statistics for Data Science**

:- Combinatorics

:- Axioms

:- Variance and Expectation

:- Random Variables

:- Conditional and Joint Distributions

## Student Reviews

## Bhawana

## Fabulous NLP + ML course

I have eleven plus years of experience taking training courses. I do not usually complete surveys.

Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.

## Harish

## Excellent ML course!

The course was well structured and easy to understand. Good pace of learning.

The institute believes to provide knowledge as well as guidance in detail to each & every student.

I completed my ML course from the institute. Their international exp does help a lot !

Thanks for the training sir.