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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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 :

  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 !!

What You Will 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

Who Should Attend!

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