The Essential Machine Learning Foundations: Math, Probability, Statistics, and Computer Science (Video Collection)

An outstanding data scientist or machine learning engineer must master more than the basics of using ML algorithms with the most popular libraries, such as scikit-learn and Keras. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory is essential, which includes a working understanding of the foundational subjects of linear algebra, calculus, probability, statistics, data structures, and algorithms.

When the foundations of machine learning are firm, it becomes easier to make the jump from general ML principles to specialized ML domains, such as deep learning, natural language processing, machine vision, and reinforcement learning. The more specialized the application, the more likely its implementation details are available only in academic papers or graduate-level textbooks, either of which assume an understanding of the foundational subjects.

This master class includes the following courses:
Linear Algebra for Machine Learning
Calculus for Machine Learning LiveLessons
Probability and Statistics for Machine Learning
Data Structures, Algorithms, and Machine Learning Optimization
Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications.

Calculus for Machine Learning LiveLessons introduces the mathematical field of calculus—the study of rates of change—from the ground up. It is essential because computing derivatives via differentiation is the basis of optimizing most machine learning algorithms, including those used in deep learning, such as backpropagation and stochastic gradient descent.

Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and statistical modeling, with a focus on machine learning applications.

Data Structures, Algorithms, and Machine Learning Optimization LiveLessons provides you with a functional, hands-on understanding of the essential computer science for machine learning applications.

About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at Columbia University, New York University, leading industry conferences, via O'Reilly, and via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010; his papers have been cited more than a thousand times.

Course Requirements
Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information—such as understanding charts and rearranging simple equations—then you should be well-prepared to follow along with all of the mathematics.
Programming: All code demos are in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.