Math for Machine Learning

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"This track provides a refresher on continuous mathematics for Machine Learning students. The presentation, motivation, etc., are all from a machine learning perspective. The hope, however, is that it’s useful in other contexts. The two major topics covered are linear algebra and calculus." Mohamed Elgendy

About this track

About me:

Hi, my name is Mohamed Elgendy. I'm the author of Grokking Deep Learning for Computer Vision book. I'm currently heading the engineering efforts at Synapse,  a leading AI company that builds proprietary computer vision applications to detect threats at security checkpoints worldwide. 


What is this track about?

This track guides you through the main mathematics and statistics concepts that you need to understand to make sense of AI and Machine Learning applications. 

By the end of this training you will:

  • Understand the concept of Derivatives and how to calculate them
  • Learn the different types of Regression Analysis and how they are used in ML
  • Fit regression models using Least Squares and Gradient Descent
  • Use Softmax and Sigmoid functions
  • Identifying underfitting and overfitting by understanding the Bias-Variance tradeoff
  • Learn Vectors and Matrices
  • Calcular Eigen Values, Principal component analysis (PCA), and singular value decomposition (SVD)

This track is best suited for:

  • Students with no Mathematics or Engineering background
  • Professionals intending to study Machine Learning
  • Data Scientists

Track at a glance

Lessons 20
Commitment 4 hours
Language English
Mentor Response Time 5 hours Hrs.
Student Reviews (145)


  • Readings/Downloads Lesson 1: Introduction
  • Video Lesson 2: What is a Derivative?
  • Video Lesson 3: How to calculate Derivatives
  • Readings/Downloads Lesson 4: Regression Analysis
  • Video Lesson 5: Linear Regression
  • Video Lesson 6: Multiple Linear Regression
  • Readings/Downloads Lesson 7: Polynomial Regression
  • Video Lesson 8: Logistic Regression
  • Video Lesson 9: Least Squares Method
  • Readings/Downloads Lesson 10: Gradient Descent
  • Video Lesson 11: How to choose the Learning Rate?
  • Readings/Downloads Lesson 12: Regression Evaluation Metrics
  • Video Lesson 13: Softmax Regression & Sigmoid Function
  • Video Lesson 14: Feature Scaling
  • Video Lesson 15: Overfitting and Underfitting
  • Video Lesson 16: Matrices and Vectors - WIP
  • Readings/Downloads Lesson 17: Eigen Values, PCS, SVD
  • Video Lesson 18: Machine Learning Roadmap - WIP
  • Readings/Downloads Lesson 19: Numpy, Pandas, Scikit, matplotlib, Tensorflow - WIP
  • Readings/Downloads Lesson 20: Additional Resources & Cheat Sheets
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