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Mathematics for Machine Learning and Data Science Specialization

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

Applied Learning Project

By the end of this Specialization, you will be ready to:

Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence

Apply common vector and matrix algebra operations like dot product, inverse, and determinants

Express certain types of matrix operations as linear transformations

Apply concepts of eigenvalues and eigenvectors to machine learning problems

Optimize different types of functions commonly used in machine learning

Perform gradient descent in neural networks with different activation and cost functions

Describe and quantify the uncertainty inherent in predictions made by machine learning models

Understand the properties of commonly used probability distributions in machine learning and data science

Apply common statistical methods like MLE and MAP

Assess the performance of machine learning models using interval estimates and margin of errors

Apply concepts of statistical hypothesis testing

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Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.

Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works.

This is a beginner-friendly program with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.

Luis Serrano

Luis Serrano

Instructor, Deep Learning AI

AI scientist, popular YouTuber, and author of Grokking Machine Learning. I am a quantum AI research scientist at Zapata Computing in Toronto, Canada, developing machine learning algorithms to work in quantum computers. Before that, I lived in Silicon Valley, where I worked at the following companies: Apple: I was a lead AI educator, in charge of teaching machine learning to the employees and doing internal consulting in AI related projects. Udacity: I was the head of content for AI and Data Science, managing the team that created online courses in AI, ML, Deep Learning, Data Science, etc. Google: I was part of the video recommendations team at YouTube, where we trained machine learning algorithms to recommend videos in the main page. Before my life in technology, I was a research mathematician. I did a Bachelors and Masters at the University of Waterloo, a PhD at the University of Michigan, and an NSERC Postdoctoral Fellowship at the Université du Québec à Montréal. My love for math goes way back. As a high school student, I participated in the International Mathematical Olympiads, representing my native country of Colombia in the IMO 98 and IMO 99.

Elena Sanina

Elena Sanina

Curriculum Engineer, DeepLearning.AI


Hi! I joined DeepLearning.AI as a Curriculum Engineer to help create and revise technical assignments, aiming to improve learners’ experience with the courses. Online education can be life-changing for people, and I am excited to join the development team. I grew up in Russia, where I studied pure mathematics, then worked in Finance and Data Analysis. In 2011 I came to Australia for research work, and I have been teaching mathematical and statistical subjects at Australian Universities. A few years ago, I started learning Data Science, moved to the software development industry, and joined the DLAI team. I love mathematics (with programming and teaching), dancing, and people. Most of my free time I have spent in the dance studios. On weekends I teach Character Dance in the local Australian ballet schools, help some children with mathematics, hike, and now learn to swim and dance in the water.

Anshuman Singh

Anshuman Singh

Curriculum Architect. DeepLearning.AI

Anshuman joined as a Curriculum Architect at DeepLearning.ai. Before joining DeepLearning.ai, he was an Associate Professor and Program Coordinator at the University of Missouri St. Louis where he lead the development of undergraduate and graduate curricula in cybersecurity, artificial intelligence, and big data. Anshuman’s research expertise is at the intersection of cybersecurity and artificial intelligence and has published and presented his research at top journals and conferences. He has taught courses on Software Assurance, Artificial Intelligence, Big Data and Ethical Hacking. In the past, he has also worked as a research scientist on DARPA and Air Force funded research projects on the application of artificial intelligence in cybersecurity. He earned his PhD in Computer Science from the University of Louisiana at Lafayette. Anshuman is also a certified (ethical) hacker and holds the CISSP and GPEN certifications. In his free time, he loves playing with electronic sound design and synthesis tools.

Magdalena Bouza

Magdalena Bouza

Curriculum Architect. DeepLearning.AI

Hi! I’m Magui (or Maggie) and I joined DeepLearning.AI as a Curriculum Engineer. I’ve been teaching in the University of Buenos Aires for 7 years now, and in the last year, I’ve also started teaching at a master’s degree program from the same university. I mostly teach subjects related to probability and statistics, and derived applications. I studied electronic engineering, and currently find myself working to finish my Doctorate degree. Additionally I have some experience in consulting, oriented to bringing data science solutions to different clients. While I’m no stranger to teaching, online learning is pretty new to me and thus represents an exciting challenge! I see this as an incredible opportunity to improve myself and how I envision teaching. I live with my partner and our two adorable cats in Buenos Aires. Besides working, which currently takes quite a bit of my time, I love traveling and meeting new people. I also enjoy cooking very much, as well as trying new dishes from different cuisines. As a hobby I practice aerial dance, where I’m able to run on walls and pretty much fly!