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Math for Data science,Data analysis and Machine Learning

Math for Data science,Data analysis and Machine Learning

Math for Data science,Data analysis and Machine Learning

Learn Math essentials for Data science,Data analysis,Machine Learning and Artificial intelligence


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What you'll learn

  • Learn the foundational concepts of Linear Algebra
  • Learn the foundational concepts of statistics
  • Learn the foundational concepts of Geometry
  • Learn the foundational concepts of Calculus
  • Application of key mathematical topics

Description

In this course, we will learn Math essentials for Data science,Data analysis and Machine Learning.  We will also discuss the importance of Linear Algebra,Statistics and Probability,Calculus and Geometry in these technological areas. Since data science is studied by both the engineers and commerce students ,this course is designed in such a way that it is useful for both beginners as well as for advanced level. The lessons of the course is also beneficial for the students of Computer science /artificial intelligence and those learning Python programming.

Here, this course covers the following areas :

Importance of Linear Algebra

Types of Matrices

Addition of Matrices and its Properties

Matrix multiplication and its Properties

Properties of Transpose of Matrices

Hermitian and Skew Hermitian Matrices

Determinants ; Introduction

Minors and Co factors in a Determinant

Properties of Determinants

Differentiation of a Determinant

Rank of a Matrix

Echelon form and its Properties

Eigenvalues and Eigenvectors

Gaussian Elimination Method for finding out solution of linear equations

Cayley Hamilton Theorem

Importance of Statistics for Data Science

Statistics : An Introduction

Statistical Data and its measurement scales

Classification of Data

Measures of Central Tendency

Measures of Dispersion: Range, Mean Deviation, Std. Deviation & Quartile Deviation

Basic Concepts of Probability

Sample Space and Verbal description & Equivalent Set Notations

Types of Events and Addition Theorem of Probability

Conditional Probability

Total Probability Theorem

Baye's Theorem

Importance of Calculus for Data science

Basic Concepts : Functions, Limits and Continuity

Derivative of a Function and Formulae of Differentiation

Differentiation of functions in Parametric Form

Rolle;s Theorem

Lagrange's Mean Value Theorem

Average and Marginal Concepts

Concepts of Maxima and Minima

Elasticity : Price elasticity of supply and demand

Importance of Euclidean Geometry

Introduction to Geometry

Some useful Terms,Concepts,Results and Formulae

Set Theory : Definition and its representation

Type of Sets

Subset,Power set and Universal set

Intervals as subset of 'R'

Venn Diagrams

Laws of Algebra of Sets

Important formulae of no. of elements in sets

Basic Concepts of Functions

Graphs of real valued functions

Graphs of Exponential , Logarithmic and Reciprocal Functions

Each of the above topics has a simple explanation of concepts and supported by selected examples.

I am sure that this course will be create a strong platform for students and those who are planning for appearing in competitive tests and studying higher Mathematics .

You will also get a good support in Q&A section . It is also planned that based on your feed back, new course materials will be added to the course. Hope the course will develop better understanding and boost the self confidence of the students.

Waiting for you inside the course!

So hurry up and Join now !!

Who this course is for:

  • Students of engineering, data science, machine learning and python programming
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