Do you understand the importance of mathematics is the foundation of Machine Learning. If Yes then start looking for some of the TOP and Best Selected Free Courses of Mathematics for Machine Learning in 2020.
BEST Free Mathematics Courses For Machine Learning In 2020.
Mathematical Foundations Courses
These courses are intended to help lay the basics for learning more advanced Maths, as well as speed the development of mathematical thinking.
Introduction to Logic, Stanford (133,478 already enrolled)
This course is an introduction to Logic from a computational perspective. It shows how to encode information in the form of logical sentences; it shows how to reason with information in this form; and it provides an overview of logic technology and its applications – in mathematics, science, engineering, business, law, and so forth.
Introduction to Mathematical Thinking, Stanford (244,831 already enrolled)
Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems.
Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world.
Calculus is the introductory math course at MIT. Freshmen arriving for their first year are expected to have already taken calculus.
Highlights for High School offers many calculus resources, listed below, as well as some additional math courses appropriate for high school students.
This course parallels the combination of theory and applications in Professor Strang’s textbook Introduction to Linear Algebra. The course picks out four key applications in the book: Graphs and Networks; Systems of Differential Equations; Least Squares and Projections; and Fourier Series and the Fast Fourier Transform.
This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.
In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you’ll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries.
Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors.
Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units.
They perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.
Introduction to Calculus, University of Sydney (25,586 already enrolled!)
The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering and commerce.
The course emphasises the key ideas and historical motivation for calculus, while at the same time striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational calculus.
Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles.
The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.
Probabilistic models use the language of mathematics. But instead of relying on the traditional “theorem-proof” format, we develop the material in an intuitive — but still rigorous and mathematically-precise — manner.
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.
You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the
Mathematics for Machine Learning
Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
You’re not alone. machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician.
Mathematics for Machine Learning, Imperial College London (Specialization)
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science.
This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data