Master the Mathematics Behind AI and Unlock Your Potential

Mathematics for Machine Learning and Data Science is a beginner-friendly specialization where you’ll master the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

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What you’ll get from this course

  • A deep understanding of what makes algorithms work, and how to tune them for custom implementation.
  • Statistical techniques that empower you to get more out of your data analysis.
  • Skills that employers desire, helping you ace machine learning interview questions and land your dream job.


Luis Serrano

Luis Serrano

Serrano Academy

Who should join?

This is a beginner-friendly course for anyone who wants to develop their mathematical fundamentals for a career in machine learning and data science. A high-school level of mathematics will help learners get the most out of this class.

Enroll now and take your career to the next level!

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Concepts you will learn

  • Data Analysis
  • Calculus
  • Vectors and Matrices
  • Matrix product
  • Linear Transformations
  • Rank, Basis, and Span
  • Eigenvectors and Eigenvalues
  • Derivatives
  • Gradients
  • Optimization
  • Gradient Descent
  • Gradient Descent in Neural Networks
  • Newton’s Method
  • Probability
  • Random Variables
  • Bayes Theorem
  • Gaussian Distribution
  • Variance and Covariance
  • Sampling and Point Estimates
  • Maximum Likelihood Estimation
  • Bayesian Statistics
  • Confidence Intervals
  • Hypothesis Testing


Course Slides

Download the course slides for the Mathematics For Machine Learning & Data Science Specialization. A specialization that teaches you the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

*Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes.

What Learners From Previous Courses Say About DeepLearning.AI

Jan Zawadzki

“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”

Jan Zawadzki
Data Scientist at Carmeq
Kritika Jalan

“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”

Kritika Jalan
Data Scientist at Corecompete Pvt. Ltd.
Chris Morrow

“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”

Chris Morrow
Sr. Product Manager at Amazon
Luo Yuzheng

“As a Behavioral Scientist, I was able to adopt methods to understand my customers better, overcome the traditional ‘one-size-fits-all’ approach, and design interventions which account for personality and individual differences.”

Luo Yuzheng
Assistant Director, Monetary Authority of Singapore
Chirag Godawat

“I gained confidence in my knowledge of machine learning. Since then, I’ve become a machine learning mentor, got a research paper published in IEEE, decided to pursue my Masters in Machine Learning, and was able to land a job at JP Morgan Chase.”

Chirag Godawat
Data Engineer, Vista
Hsin-Wen Chang

“The Machine Learning course became a guiding light. Andrew Ng explains concepts with simple visualizations and plots. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company.”

Hsin-Wen Chang
Sr. C++ Developer, Zealogics

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