Math for Machine Learning

Online | Self-paced | Start Anytime
Fundamental
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About the Course

The Math for Machine Learning course is ideal for those curious about the mathematical foundations of machine learning models. You’ll dive into key topics like probability distributions, sampling methods, hypothesis testing, linear algebra, matrix operations, and calculus-based optimization. Each concept is tied back to machine learning models, showing how these calculations are applied in pre-built libraries. By the end of the course, you’ll gain a deeper mathematical understanding of the mechanics behind various machine learning models.

Curriculum

  • Module 0: Getting Started
    DESCRIPTION

    Overview:

    This module shows the necessary setup and importance of math in ML

    Topics to Cover:

    • Importance of math calculations
    • Understand the workings behind ML models

  • Module 1: Fast Track
    DESCRIPTION

    Overview:

    This module covers the general mathematical concepts that are involved in the Machine Learning process.

    Topics to Cover:

    • Statistics
    • Linear Algebra
  • Module 2: Statistics - Distributions
    DESCRIPTION

    Overview:

    This module explains the essential statistics distributions used in data science.

    Topics to Cover:

    • Understand the different types of distribution
    • Learn the formula in calculating probability
    • Application of the distributions in data science
  • Module 3: Statistics - Sampling
    DESCRIPTION

    Overview:

    This module teaches the definition of key terms and different sampling techniques.

    Topics to Cover:

    • Population vs Sample
    • Central Limit Theorem
    • Various Sampling Techniques
    • Application of the Sampling Techniques

  • Module 4: Statistics - Hypothesis Testing
    DESCRIPTION

    Overview:

    This module explores the various methods to test hypothesis.

    Topics to Cover:

    • Understand basic Z-test and T-test
    • One sample vs two sample tests
    • Advanced hypothesis testing such as Chi-Squared, ANOVA, and MANOVA
  • Module 5: Linear Algebra - Vector and Matrix Operations
    DESCRIPTION

    Overview:

    This module explains the basics of vector and matrix operations.

    Topics to Cover:

    • Vector addition, subtraction, multiplication
    • Matrix addition, subtraction, multiplication
    • Applications of Vector and Matrix operations in ML
  • Module 6: Linear Algebra - Solving System of Linear Equations
    DESCRIPTION

    Overview:

    This module demonstrates working with linear equations to find possible solutions.

    Topics to Cover:

    • Why Linear Equations?
    • Singular Systems
    • Underdetermined or overdetermined systems
    • Sparse Matrices
  • Module 7: Linear Algebra - Linear Regression and Collinearity
    DESCRIPTION

    Overview:

    This module applies mathematical concepts in linear regression to real-life scenario.

    Topics to Cover:

    • Simple and Multiple Linear Regressions
    • Collinearity effects
    • Application in Housing data
    • Variance Inflation Factor
  • Module 8: Linear Algebra - Eigen Values and Eigen Vectors
    DESCRIPTION

    Overview:

    This module examines the effects of eigen values and eigen vectors on ML models.

    Topics to Cover:

    • What are Eigen values and Eigen vectors?
    • Projection of one vector onto another
    • Application of Eigen Values and Eigen Vector
  • Module 9: Calculus - Derivatives and Higher Order Derivatives
    DESCRIPTION

    Overview:

    This module uses derivative to determine the optimal values.

    Topics to Cover:

    • Introduction to Derivatives
    • Calculating derivatives using Numpy and Scipy
    • Application of derivatives with higher order derivatives

  • Module 10: Calculus - Partial Derivatives and Gradients
    DESCRIPTION

    Overview:

    This module extends the application of derivatives into partial derivatives.

    Topics to Cover:

    • Introduction to partial derivatives
    • Calculate partial derivatives using Numpy and Scipy
    • Calculate gradients

  • Module 11: Calculus - Taylor Series
    DESCRIPTION

    Overview:

    This module applies Taylor Series in ML models.

    Topics to Cover:

    • Introduction to Taylor Series
    • Apply Taylor Series to Sin(x) functions

  • Module 12: Optimization - Gradient Descent
    DESCRIPTION

    Overview:

    This module optimizes the models using gradient descent.

    Topics to Cover:

    • What is Gradient Descent?
    • Application to one and two variables
    • Application to linear regression

Learning Outcomes

By the end of this course, participants will be able to:

  • Demonstrate proficiency in key statistical distributions and utilize them effectively in real-world applications
  • Apply appropriate sampling techniques and interpret the results
  • Conduct hypothesis testing using both basic and advanced methods
  • Perform vector and matrix operations relevant to machine learning
  • Optimize machine learning models using calculus-based technique

Tools

Python
Early Access Available!

Get early access to this course at a special rate. Not all content is ready yet, but you can sign up today and unlock new materials as they are released!

Machine Learning Operations (MLOps)
Original price was: $699.00.Current price is: $350.00.
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FAQ

Frequently asked questions about the bootcamp

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

You can get support in multiple ways:

  • TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
  • Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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