Math for Data science,Data analysis and Machine Learning

Math for Data science,Data analysis and Machine Learning

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 :

1. Importance of Linear Algebra

2. Types of Matrices

3. Addition of Matrices and its Properties

4. Matrix multiplication and its Properties

5. Properties of Transpose of Matrices

6. Hermitian and Skew Hermitian Matrices

7. Determinants ; Introduction

8. Minors and Co factors in a Determinant

9. Properties of Determinants

10. Differentiation of a Determinant

11. Rank of a Matrix

12. Echelon form and its Properties

13. Eigenvalues and Eigenvectors

14. Gaussian Elimination Method for finding out solution of linear equations

15. Cayley Hamilton Theorem

16. Importance of Statistics for Data Science

17. Statistics : An Introduction

18. Statistical Data and its measurement scales

19. Classification of Data

20. Measures of Central Tendency

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

22. Basic Concepts of Probability

23. Sample Space and Verbal description & Equivalent Set Notations

24. Types of Events and Addition Theorem of Probability

25. Conditional Probability

26. Total Probability Theorem

27. Baye's Theorem

28. Importance of Calculus for Data science

29. Basic Concepts : Functions, Limits and Continuity

30. Derivative of a Function and Formulae of Differentiation

31. Differentiation of functions in Parametric Form

32. Rolle;s Theorem

33. Lagrange's Mean Value Theorem

34. Average and Marginal Concepts

35. Concepts of Maxima and Minima

36. Elasticity : Price elasticity of supply and demand

37. Importance of Euclidean Geometry

38. Introduction to Geometry

39. Some useful Terms,Concepts,Results and Formulae

40. Set Theory : Definition and its representation

41. Type of Sets

42. Subset,Power set and Universal set

43. Intervals as subset of 'R'

44. Venn Diagrams

45. Laws of Algebra of Sets

46. Important formulae of no. of elements in sets

47. Basic Concepts of Functions

48. Graphs of real valued functions

49. 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 !!

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

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What you will learn

• Learn the foundational concepts of Linear Algebra
• Learn the foundational concepts of statistics
• Learn the foundational concepts of Geometry

Rating: 4.45238

Level: Expert Level

Duration: 19.5 hours

Instructor: Sandeep Kumar Mathur