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Maths for Data Science by DataTrained
Rating: 3.9 out of 5(337 ratings)
24,126 students

Maths for Data Science by DataTrained

Explore the application of key mathematical topics related to linear algebra with the Python programming language
Created byDt Evolve
Last updated 3/2019
English

What you'll learn

  • Explore the application of key mathematical topics related to linear algebra with the Python programming language
  • Perform linear and logistic regressions in Python
  • Apply your skills to real-life business cases
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

Explore related topics

Course content

1 section • 13 lectures • 55m total length

  • Introduction1:09

    Overview: Explore the application of key mathematical topics related to linear algebra with the Python programming language.

    Expected Duration: After completion of this course, you should be able to accomplish the objectives from the following lessons and topics.


    Lessons on Math for Data Science & Machine Learning:


    1. Understand how to work with vectors in Python


    2. Basis and Projection of Vectors: Understand the Basis and Projection of Vectors in Python


    3. Work with Matrices: Understand how to work with matrices in Python


    4. Matrix Multiplication: Understand how to multiply matrices in Python


    5. Matrix Division: Understand how to divide matrices in Python


    6. Linear Transformations: Understand how to work with linear transformations in Python


    7. Gaussian Elimination: Understand how to apply Gaussian Elimination


    8. Determinants: Understand how to work with determinants in Python


    9. Orthogonal Matrices: Understand how to work with orthogonal matrices in Python


    10. Eigenvalues: Recognize how to obtain eigenvalues from eight decompositions in Python


    11. Eigenvectors: Recognize how to obtain eigenvectors from eigendecomposition in Python


    12. PseudoInverse: Recognize how to obtain pseudoinverse in Python


    13. Exercise: Math for Data Science and Machine Learning

  • Understand how to work with vectors in Python5:23

    This tutorial will help you with the Basis and Projections of Vectors in Data Science.

  • Understand the Basis and Projection of Vectors in Python5:23

    Understand the Basis and Projection of Vectors in Python

  • Work with Matrices3:34

    Understand how to work with matrices in Python

  • Matrix Multiplication4:33

    Understand how to multiply matrices in Python

  • Matrix Division4:30

    Understand how to divide matrices in Python

  • Linear Transformations3:28

    Understand how to work with linear transformations in Python

  • Gaussian Elimination2:48

    Understand how to apply Gaussian Elimination

  • Determinants5:14

    Understand how to work with determinants in Python

  • Orthogonal Matrices4:52

    Understand how to work with orthogonal matrices in Python


  • Eigen values5:45

    Recognize how to obtain eigenvalues from eight decompositions in Python


  • Eigenvectors4:29

    Recognize how to obtain eigenvectors from eigen decomposition in Python

  • PseudoInverse4:06

    Recognize how to obtain pseudoinverse in Python

Requirements

  • 10th class Level math knowledge is expected

Description

This course offers a comprehensive exploration of linear algebra, specifically tailored for application in data science and machine learning using Python. Upon completing this course, participants will gain proficiency in the following areas:

  • Mathematical Foundations for Data Science and Machine Learning: A foundational overview of essential mathematical concepts.

  • Vector Operations in Python: Learning to manipulate vectors within the Python programming environment.

  • Basis and Projection of Vectors: A deep dive into understanding and implementing vector basis and projection techniques in Python.

  • Matrix Operations: Developing skills to handle matrix operations, including working with, multiplying, and dividing matrices in Python.

  • Linear Transformations: Gaining an understanding of linear transformations and how to implement them using Python.

  • Gaussian Elimination: Mastering the application of Gaussian elimination in problem-solving.

  • Determinants: Exploring the calculation and application of determinants in Python.

  • Orthogonal Matrices: Understanding and working with orthogonal matrices within the Python framework.

  • Eigenvalues and Eigenvectors: Recognizing and computing eigenvalues and eigenvectors through eigendecomposition in Python.

  • Pseudoinverse Computation: Learning to calculate pseudoinverse matrices in Python.

Each topic is designed to build upon the last, ensuring a thorough understanding of how linear algebraic concepts can be effectively applied in Python for data science and machine learning applications. By the end of the course, participants will have a robust set of skills to tackle real-world problems in these fields.

Who this course is for:

  • Beginner python developers looking for a data science career

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