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You’re a data science professional looking to better understand fundamental math concepts.
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You wish to become a better ML practitioner by understanding the role of probability, statistics, hypothesis testing, linear algebra, and calculus in ML.
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You work with data scientists, ML practitioners, or statisticians who frequently use mathematical models in their daily work.
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You’re an analyst, a project manager working with data science teams, or a software engineer interested in numerical computing.
Math Fundamentals for Machine Learning and Data Science
Recognizing foundational math concepts in your data science career is key to understanding the underpinnings of many ML algorithms and models. Statistics, probability, and linear algebra form the backbone of data science and ML, enabling practitioners to extract meaningful insights from data, make informed decisions, and build robust models. Upon successful completion of this learning path, you’ll be equipped with essential math concepts, empowering you to navigate the complexities of data science and ML with precision and confidence.
By the end of this learning path, you’ll learn:
Core principles, such as gradient descent and regression models, that are crucial for model formulation, training, and evaluation.
Fundamental math concepts that underpin AI, empowering you to build and deploy AI solutions at a more profound and impactful level.
How mastery of math concepts can help you improve your analytical and problem-solving skills, which are imperative to address challenges encountered in AI/ML projects.
Statistics and Hypothesis Testing
Learn fundamental skills to describe and analyze data with Python.
What's Next?
Complete all courses in this path to earn your course completion certificates.
This is for you because...
Prerequisites
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Get Started with Anaconda course (optional)
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Basic Python proficiency (e.g., variables, loops, collections, and functions) and familiarity with library usage.
INSTRUCTOR
Thomas Nield
Thomas is the Founder of Nield Consulting Group and Yawman Flight, and an instructor at University of Southern California. He has authored bestselling books, including Essential Math for Data Science (O’Reilly).
