12 Best Math Courses for Machine Learning and Data Science in 2024

Are you looking for the Best Math Courses for Machine Learning and Data Science? But confused because of so many courses available online. So, don’t worry. Your search will end after reading this article. In this article, you will find the 12 Best Math Courses for Machine Learning and Data Science. So, give your few minutes to this article and find the Best Math Courses for Machine Learning and Data Science.

Knowledge of Mathematics is essential for understanding how machine learning and its algorithms work. In math, the most important topics are-

• Probability and Statistics
• Linear Algebra
• Calculus
• Matrix

This image from Built In shows the importance of each mathematic topic for Machine Learning.

So, to learn or brush up on these topics, I have chosen the 12 Best Math Courses for Machine Learning and Data Science. First, let me tell you, by what criteria these courses are “Best Math Courses”-

Criteria-

1. Rating of these Courses.
2. Coverage of Topics.
3. Engaging trainer and Interesting lectures.
4. Number of Students Benefitted.
5. Good Reviews from various aggregators and forums.

So, without wasting your time, let’s start finding the Best Math Courses for Machine Learning and Data Science for you.

Best Math Courses for Machine Learning and Data Science

1. Mathematics for Machine Learning Specialization– Coursera

Rating-	4.4/5
Provider-	Imperial College London
Time to Complete-	4 Months (4 hours/week)
FREE/Paid–	Paid

This Mathematics for Machine Learning Specialization is one of the best specialization programs that covers all mathematical topics required for Machine Learning. This specialization program is a 3-course series.

To learn Linear Algebra concepts such as vectors, matrices, etc, and Calculus concepts such as Regression, optimization, Taylor series, and linearisation, this specialization program is good.

The last course of this program is on Principal Component Analysis (PCA). Principal Component Analysis(PCA) is an unsupervised algorithm. Also, it is the most popular dimensionality Reduction Algorithm.

This specialization program also has various exercises and hands-on practices to strengthen your understanding.

These are the 3 courses-

Courses Include-

1. Mathematics for Machine Learning: Linear Algebra
2. Mathematics for Machine Learning: Multivariate Calculus
3. Mathematics for Machine Learning: PCA

Pros-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Provides enough knowledge of Linear Algebra.
• Their quizzes and assignments are engaging and helpful.

Cons-

• The last course of this program lacks in quality compared to the first two courses.

Now, let’s see whether you should enroll in this specialization program or not.

You Should Enroll if-

• The program claims that this is suitable for beginners but I observed that previous Linear Algebra Knowledge is required. And basic knowledge of Python and NumPy is required for Course 3.

Interested to Enroll?

If yes, then check out all details here- Mathematics for Machine Learning Specialization

2. Intro to Statistics– Udacity

Rating-	NA
Provider-	Udacity
Time to Complete-	2 Months
FREE/Paid–	FREE

This Intro to Statistics is a completely FREE course for beginners. The founder of Udacity “Sebastian Thrun” will teach in this course. If you are a beginner and want to learn statistics from scratch, I would suggest this course. Because this course is completely free and you will learn from the basics.

I found various free courses that lack content quality but this course has a lot of content. There are 34 lessons in this course where you will learn Scatter Plots, Bar Charts, Pie Charts, Probability, Bayes Rule, Central Limit Theorem, Normal Distribution, and many more.

This course not only covers theoretical concepts but also has exercises and case studies. There are various problem sets in this course for eg. problem set on Regression and Correlation, problem set on Probability, etc.

Pros-

• For this course, you don’t need to pay any amount.
• You will learn from an experienced instructor, Sebastian Thrun.

Cons-

• After completing this course, you will not receive a certificate.

You Should Enroll if-

• You are a beginner, but it’s good if you have already heard of some easy statistical concepts.

Interested to Enroll?

If yes, then start learning- Intro to Statistics

3. Linear Algebra Refresher Course– Udacity

Rating-	NA
Provider-	Udacity
Time to Complete-	4 Months
FREE/Paid–	FREE

This Linear Algebra Refresher Course with Python is a Free course to learn linear algebra basics. This is not a very long course. It has only 4 lessons.

In this course, you will learn Points and Vectors and how to identify Points & Vectors. For learning vectors, there is a separate lesson in this course, where you will learn The Vector Module, Plus, Minus, Scalar Multiply, Magnitude and Direction, Inner Products, Coding Dot Product & Angle, Projecting Vectors, and Cross Products.

The last two lessons are on Intersections and Matrices and Transformation of State.

In Matrices and Transformation of State, you will learn about Kalman Prediction, Vectors in Python, Coding Matrices, etc. The instructor uses Python Programming Language to teach these concepts.

Pros-

• This course is completely free.
• Work on hands-on practices.
• Cover in-depth Linear Algebra concepts.

Cons-

• This course doesn’t provide a certificate of completion.

You Should Enroll if-

• You have experience with Python programming languages.

Interested to Enroll?

If yes, then start learning- Linear Algebra Refresher Course with Python

4. Data Science Math Skills- Coursera

Rating-	4.5/5
Provider-	Duke University
Time to Complete-	13 hours
FREE/Paid–	Paid

This Data Science Math Skills course is offered by Duke University. As the name sound, this course is designed for math required for data science.

In this course, first, you will learn Sets, Numbers, and Sigma Notation. The best thing I found in this course is that this course doesn’t cover only the theoretical part. There are various quizzes in this course. You will find a graded quiz on Sets, Numbers, Sigma Notation, etc.

You will also learn Cartesian Plane, Functions, and Tangent Lines.

This course will also teach Probability and covers Permutations and Combinations, Bayes’ Theorem, etc. Overall, this is a great course to learn Linear Algebra.

Pros-

• You will get a Shareable Certificate upon completion.
• This course also has quizzes and practical assignments.

Cons-

• The instructor doesn’t explain probability concepts in detail.

Now, let’s see whether you should enroll in this course or not.

You Should Enroll if-

• You have no prior knowledge of Math and want to learn the basics of Math required for Machine Learning and Data Science.

Interested to Enroll?

If yes, then check out all details here- Data Science Math Skills

5. Introduction to Calculus- Coursera

Rating-	4.8/5
Provider-	The University of Sydney
Time to Complete-	51 hours
FREE/Paid–	Paid

This Introduction to Calculus is dedicated to Calculus. In this course, you will get a complete understanding of Calculus. There are 5 modules in this course.

The course is combined with videos, quizzes, and notes. In the first two modules, you will learn precalculus and functions. You will find a lot of exercises throughout this course.

Limits and Derivatives are explained in modules 3 and 4. And the last and 5th module covers integral calculus. The quizzes are not easy to complete and you might need extra resources to finish the quizzes.

But, Overall this is a great course to dive deeper into calculus required for machine learning and data science. The instructor of this course explains the concepts very easily and his pace of teaching is just perfect.

Pros-

• You will get a Shareable Certificate upon completion.
• Covers calculus in detail.
• Uses quizzes and exercises to understand the concepts.

Cons-

• The quizzes are hard to finish.

Now, let’s see whether you should enroll in this course or not.

You Should Enroll if-

• You want to learn Calculus but have some previous knowledge.

Interested to Enroll?

If yes, then check out all details here- Introduction to Calculus

6. The Math of Data Science: Linear Algebra– edX

Rating-	NA
Provider-	RICE University
Time to Complete-	8 Weeks( If you spend 6-8 hours per week)
FREE/Paid–	Paid

This “The Math of Data Science: Linear Algebra“ is another best course to learn Linear algebra for data science. The best thing about this course is that it tries to make a balance between theory and practice.

The instructor explains the theory as well as covers hands-on practices. edX is one of the most popular data science and machine learning platforms. Their courses are from Top Universities across the globe.

Throughout this course, you will learn the essential concepts of Linear Algebra such as Linear equations, vectors, matrices, etc. The instructor not only covers these topics but also teaches how to extract useful information from data using Linear Algebra.

Pros-

• You will get a Shareable Certificate upon completion.
• Work on real-world problems.
• Accredited certificate.

Cons-

• edX enrollment process is confusing.

Who Should Enroll?

• Those who studied High school algebra.

Interested to Enroll?

If yes, then check out all details here- The Math of Data Science: Linear Algebra

7. Probabilistic Graphical Models Specialization- Coursera

Rating-	4.6/5
Provider-	Stanford University
Time to Complete-	4 Months ( 11 hours/week)
FREE/Paid–	Paid

This Probabilistic Graphical Models Specialization is an advanced-level program. This is not for beginners. In this program, you will learn about probabilistic graphical models. This program required previous knowledge of Probability, previous programming experience, and a basic understanding of Data Structure and Algorithms.

This program has 3 courses. And in this program, you will learn fundamental methods in probabilistic graphical models and the real-world applications related to probabilistic graphical models.

This program is more practical in nature. There are different quizzes and exercises in this program. To complete this program, you must give proper attention to the material because this program covers complex topics.

Pros-

• You will get a Shareable Certificate and Course Certificates upon completion.
• You will work on Real-World Projects.
• And you will learn advanced topics.

Cons-

• The support is not good. It’s hard to get a reply from the instructor if you have any doubts.

You Should Enroll if-

• You have some mathematical skills and Programming skills.
• And some previous knowledge of basic concepts in discrete probability theory (independence, conditional independence, and Bayes’ rule) will also help.

Interested to Enroll?

If yes, then check out all details here- Probabilistic Graphical Models Specialization

8. Statistics with R Specialization– Coursera

Rating-	4.6/5
Provider-	Duke University
Time to Complete-	7 Months (3 hours/week)
FREE/Paid–	Paid

This Statistics with R Specialization program is good for learning Statistical concepts using R Programming. This program has 3 courses. This program is designed for those who are interested in Data Analytics and Statistics.

Throughout this program, you will learn the concepts of statistics and data analytics and how to perform data analysis using R. The first course of this program will teach the Probability basics and the second course is all about inferential statistics. In this second course, you will get to know about Central Limit Theorem and Confidence Interval.

The last course will teach Regression and cover Linear Regression and Multiple Regression. Along with the theoretical concepts, you will work on quizzes and exercises in all three courses.

These are the 3 courses-

Courses Include-

1. Introduction to Probability and Data with R
2. Inferential Statistics
3. Linear Regression and Modeling

Pros-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Provides Practice Quizzes and Graded Assignments with Peer Feedback.

Cons-

• The program doesn’t provide enough knowledge about R Programming.

You Should Enroll if-

• You have basic math knowledge. It is good to have previous R Programming knowledge.

Interested to Enroll?

If yes, then check out all details here- Statistics with R Specialization

9. Probability and Statistics- Coursera

Rating-	4.6/5
Provider-	University of London
Time to Complete-	18 Hours
FREE/Paid–	Paid

This Probability and Statistics course is especially dedicated to Probability and Statistics. This is a basic course that covers Probability and Statistics. This course is more theoretical in nature.

This course will clear your doubts regarding Probability and Statistics. The explanation method was unique and helpful. This course has video tutorials and reading materials. That means you have to read some extra materials after each module.

This course uses various helpful examples to explain the topic which is helpful for students.

Pros-

• You will get a Shareable Certificate upon completion.
• For beginners, this course is good to understand Probability and Statistics using examples.

Cons-

• Only covers theory about Statistics and Probability.

You Should Enroll if-

• You are a beginner and want to learn Probability and Statistics.

Interested to Enroll?

If yes, then check out all details here- Probability and Statistics

10. Intro to Descriptive Statistics– Udacity

Rating-	NA
Provider-	Udacity
Time to Complete-	2 Months
FREE/Paid–	FREE

This Intro to Descriptive Statistics is another Free statistics course at Udacity. This course will teach how to compute a value from data by using Google Spreadsheets.

You will also learn how to gather, organize, compute, and visualize information from a dataset. This course also has one final project, where you will use the tools you learned in the course to compute statistics hidden within a deck of cards.

There is a separate tutorial on Google Spreadsheet and Central Tendency. This course has quizzes and problem sets to get hands-on practice.

Overall, this course is good for learning descriptive statistics fundamentals.

Pros-

• This is a FREE course and you don’t need to pay.
• You will learn advanced topics and work on hands-on practices.

Cons-

• You will not receive a certificate after completing the course.

You Should Enroll if-

• You have an understanding of basic algebra and arithmetic.

Interested to Enroll?

If yes, then start learning- Intro to Descriptive Statistics

11. Mathematical Foundation For Machine Learning and AI- Udemy

Rating-	4.0/5
Provider-	Eduonix Learning Solutions
Time to Complete-	4.5 hours
FREE/Paid–	Paid

This Mathematical Foundation For Machine Learning and AI course covers three main mathematical theories: Linear Algebra, Multivariate Calculus, and Probability Theory.

But this course is not a very detailed course to learn these topics. This course is good for revising your concepts. The instructor will teach Scalars, Vectors, Derivatives, Gradients, and Probability.

I found that this is a short course that only covers basics. If you want a detailed course, then this course is not for you.

Pros-

• You will get a Certificate of completion and 6 downloadable resources.
• And you will get lifetime access to the course material.

Cons-

• The course doesn’t use the latest version of Python.

You Should Enroll if-

• You want to refresh or learn the mathematical tools required for AI and machine learning.

Interested to Enroll?

If yes, then check out all details here- Mathematical Foundation For Machine Learning and AI

12. Calculus 3 (Multivariable calculus)- Udemy

Rating-	4.8/5
Provider-	Hania Uscka-Wehlou
Time to Complete-	47.5 hours
FREE/Paid–	Paid

This Calculus 3 (Multivariable calculus) is another best course available on Udemy for Multivariable calculus. This course covers advanced calculus topics. The instructor’s explanation makes complex topics easier.

This course is the perfect balance between theory and practice. And the content quality of this course is perfect. Before enrolling in this course, you must have previous knowledge of Calculus 1, Calculus 2, and Linear Algebra. Overall, this course is good for you if you want to gain a thorough understanding of calculus.

Pros-

• You will get a Certificate of completion.
• Well-structured content.
• Along with this, you will get 300 downloadable resources and full lifetime access to the course material.

Cons-

• You might feel the pace of teaching is a bit fast.

Who Should Enroll?

• You are an engineering student or a machine learning enthusiast.

Interested to Enroll?

If yes, then check out all details here- Calculus 3 (Multivariable calculus)

So, that’s all. These are the 12 Best Math Courses for Machine Learning and Data Science. Now, it’s time to wrap up.

Conclusion

I hope these courses will give you a strong understanding of math concepts. My aim is to provide you with the best resources for Learning. If you have any doubt or questions, feel free to ask me in the comment section.

Comparison of Best Math Courses for Machine Learning/Data Science

S/N	Course Name	FREE/Paid	Rating	Provider	Time to Complete
1.	Mathematics for Machine Learning Specialization	Paid	4.4/5	Imperial College London	4 Months
2.	Intro to Statistics	FREE	NA	Udacity	2 Months
3.	Linear Algebra Refresher Course with Python	FREE	NA	Udacity	4 Months
4.	Data Science Math Skills	Paid	4.5/5	Duke University	13 hours
5.	Introduction to Calculus	Paid	4.8/5	The University of Sydney	51 Hours
6.	The Math of Data Science: Linear Algebra	Paid	NA	edX	8 Weeks( If you spend 6-8 hours per week)
7.	Probabilistic Graphical Models Specialization	Paid	4.6/5	Stanford University	4 Months
8.	Statistics with R Specialization	Paid	4.6/5	Duke University	7 Months
9.	Probability and Statistics	Paid	4.6/5	University of London	18 Hours
10.	Intro to Descriptive Statistics	FREE	NA	Udacity	2 Months
11.	Mathematical Foundation For Machine Learning and AI	Paid	4.0/5	Udemy	4.5 hours
12.	Calculus 3 (Multivariable calculus)	Paid	4.9/5	Udemy	47.5 hours

My Recommendation

Best Math Course for Beginners	Mathematics for Machine Learning Specialization
Best Math Course for Advanced Learners	Probabilistic Graphical Models Specialization
Best Linear Algebra Course	Linear Algebra Refresher Course with Python (FREE)
Best Statistics Course(For Beginners)	Intro to Statistics (FREE)
Best Calculus Course	Calculus 3 (Multivariable calculus)

Tell me in the comment section, which is the Best Math Course for Machine Learning and Data Science?

All the Best!

Happy Learning!

FAQ

Thank YOU!

Learn Machine Learning A to Z Basics

Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford