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Mathematics Courses for Machine Learning

Machine Learning is one of the advanced technologies in the IT world that requires in-depth knowledge of mathematics concepts. Knowledge of mathematics is essential to start a career in the machine learning domain. ML algorithms are entirely based on mathematics concepts such as probability, statistics, linear algebra, advanced calculus, etc. If anyone wants to accelerate their career in ML, they must have to brush up and groom their mathematics skills as well. Although there are so many courses available online but right guidance will let you to the right place to achieve your goals.

Hence, in this topic, “Maths courses for Machine Learning”, we will discuss a few best courses available over the internet. Referring to these courses, you can enhance the basic math skills required for entering the machine learning world. Below are some criteria, based on which we are suggesting to follow given mathematics courses for ML.

Criteria

• Course ratings are given by benefitted students
• Course coverage
• Trainer engagement
• Interesting lectures
• The review was suggested by various aggregators and forums.

Now, without wasting time, let’s start discovering a few best online mathematics courses for machine learning.

Best Online Mathematics courses for Machine Learning

1. Mathematics for Machine Learning Specialization
2. Data Science Math Skills
3. Introduction to Calculus
4. Probabilistic Graphical Models Specialization
5. Statistics with R Specialization
6. Probability and Statistics
7. Mathematical Foundation for Machine Learning and AI

1. Mathematics for Machine Learning Specialization

As per different reviews, this is one of the best courses provided by Coursers for a better understanding of mathematics skills for machine learning. It covers almost all mathematics topics required for ML. Moreover, this course aims to fill the gap and build an intuitive understanding of mathematics.

This course is categorised into 3-series as follows:

• In the first series, we will learn important concepts of linear algebra, vectors, matrices and their relationship with data in ML.
• In the second series, we will focus on Multivariate Calculus, which helps you in getting in-depth knowledge of optimizing fitting functions to get good fits to data.
• The last and 3^rd series of this course is Dimensionality Reduction with Principal Component Analysis. This course enables you to implement entire mathematics knowledge in real-time scenarios.

After completing all series, you will feel confident enough to start a career in machine learning.

Course description:

• Mathematics for Machine Learning: Linear Algebra
• Mathematics for Machine Learning: Multivariate Calculus
• Mathematics for Machine Learning: PCA

What you will learn:

This course will help you to learn so many important mathematics concepts such as principal component analysis, multivariate calculus, linear algebra (basics and advanced), vector calculus, gradient descent, Python, dimensionality reduction, eigenvalues and eigenvectors, etc.

Benefits of this course:

After completion of this course, you will earn Shareable Certificate and Course Certificates. Further, you will also get the entire course agenda, such as recorded video lectures, class notes, practice theoretical & programming assignments, Graded Quizzes, etc.

Pre-requisites for this course:

If you are enrolling on this course, you must have matrix level mathematics knowledge with a basic understanding of Python and NumPy.

Course Rating– 4.6 out of 5

Source– Imperial College London

Course duration– 16 weeks

Important link: Click here to enrol and know more about this course.

2. Data Science Math Skills

This course is offered by Duke University Durham (North Carolina). This course helps you in building core concepts of algebra required for machine learning, such as vocabulary, notation, concepts, and algebra rules.

Topics included in this course.

• Set theory
• Venn diagrams
• Properties of the real number line
• Sigma notation, interval notation and quadratic equations
• Concepts of a Cartesian plane, slope, and distance formulas
• Functions and graphs
• Instantaneous rate of change and tangent lines to a curve
• Logarithmic functions
• Exponential functions
• Probability
• Bayes Theorem

Benefits of this course:

You can earn a Shareable Certificate after successful completion of this course.

Pre-requisites:

To enrol on this course, you do not need a prior understanding of the maths required for ML and Data Science.

Course Rating– 4.5 out of 5

Source– Duke University Durham (North Carolina)

Course duration– 13 hours

Important link: Click here to enrol and know more about this course package.

3. Introduction to Calculus

This is one of the highest-rated maths courses over the internet by David Easdown. It covers the entire calculus concepts required for machine learning solutions. Further, this course helps you to maintain a balance between theory and the application of calculus.

This course is divided into 5-weeks plans as follows:

1^st Week: Precalculus (Setting the scene)

2^nd Week: Functions (Useful and important repertoire)

3^rd Week: Introducing the differential calculus

4^th Week: Properties and applications of the derivative

5^th Week: Introducing the integral calculus

Benefits of this course:

Upon completion of this course, you will get an electronic Certificate on your Accomplishments page.

Pre-requisites:

You must have a basic understanding of calculus and general mathematics concepts to enrol on this course. This course is significant if you only want to Master yourself in Calculus.

Rating– 4.8 out of 5

Course Provider– David Easdown (The University of Sydney)

Course Duration– 59 Hours

Important Link: Click here to enrol and know more about this course.

4. Probabilistic Graphical Models Specialization

This course is offered by Stanford University, which provides a richframework for probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other.

This course is designed in a way that will help you to learn various important skills such as inference, Bayesian Network, Belief Propagation, Graphical Model, Markov Random Field, Markov Random Field, Markov Chain Monte Carlo (MCMC), Algorithmsand Expectation-Maximization (EM) Algorithm.

The complete course includes three specializations, which are as follows:

Course 1– Probabilistic Graphical Models 1: Representation

Course 2– Probabilistic Graphical Models 2: Inference

Course 3– Probabilistic Graphical Models 3: Learning

Benefits:

• The course provides Sharable specialization and Certification after successful completion of the code.
• Self-Paced Learning Option Adaptable and flexible Learning option
• 24*7 Availability of Course videos and readings.
• Different Practice Quizzes
• Assignments with Peer Feedback
• Quizzes with Feedback with Gradings
• Programming Assignments with a Grading system

Pre-requisites:

Before enrolling on this course, one should have a basic understanding of mathematics and at least one programming knowledge.

Course Rating– 4.6/5

Course Provider– Daphne Koller (Stanford University)

Course duration– 4 Months (11 hours/week)

Important Link: Click here to enrol and know more information related to this course.

5. Statistics with R Specialization

This course is offered by Duke University under the guidance of Mine Çetinkaya-Rundel, David banks, Colin rundel, Merlise A Clyde.

This course helps you to learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modelling to understand natural phenomena and make data-based decisions. Further, it enables you to communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluate data-based decisions, and wrangle and visualize data with R packages for data analysis.

There are 5 Courses in this Specialization as follows:

• Introduction to Probability and Data with R
• Inferential Statistics
• Linear Regression and Modeling
• Bayesian Statistics
• Statistics with R Capstone

Extra Benefits:

• Shareable Specialization and Course Certificates
• Self-Paced Learning Option
• Course Videos & Readings
• Practice Quizzes
• Assignments with Peer Feedback & grades
• Quizzes with Feedback & grades
• Programming Assignments with Grades

Pre-requisites:

Before enrolling on this course, you must have prior knowledge of basic mathematics concepts, and good interest in data analysis will be an advantage. Further, no previous programming knowledge is mandatory to start this course.

Course rating: 4.6 out of 5

Course provider: Duke University

Course Duration: Approx. 7 months

Important Link: Click here to enrol and know more about this course.

6. Probability and Statistics

This course is offered by the University of London under the guidance of Dr James Abdey. This course is specially designed for probability, descriptive statistics, point and interval estimation of means and proportions, etc. It helps in building essential skills for good decision making and predicting future results.

This course includes various topics:

• Dealing with Uncertainty and Complexity in a Chaotic World
• Quantifying Uncertainty With Probability
• Describing The World The Statistical Way
• On Your Marks, Get Set, Infer!
• To p Or Not To p?
• Applications

Extra benefits:

You will be provided with a Shareable Certificate after completion of this course. Further, you will also get the entire course agenda, such as recorded video lectures, class notes, practice theoretical & programming assignments, Graded Quizzes, etc.

Pre-requisites:

This course is specially designed for beginners; hence no mathematics and programming knowledge is required to start this course.

Course rating: 4.6 out of 5

Course provider: University of London

Course duration: 16 hours

Important Link: Click here to enrol and know more about this course

7. Mathematical Foundation for Machine Learning and AI

This course is designed by Eduonix Learning Solutions on Udemy. This course enables you to learn the basic math concepts that are required for ML and also learn to implement them in R and Python.

It provides you with detailed information on some important topics of Mathematics such as Linear algebra, multivariate calculus, probability theory, etc.

Mathematics is one of the key players to develop programming skills, and this course is designed in the exact same way to help you to master the mathematical foundation required for writing programs and algorithms for AI and ML.

Course content

This course is categorised into 3 sections:

1) Linear Algebra:

It helps in understanding the parameters and structures of different ML algorithms. Further, it gives the basic idea of neural networks also. It includes various topics as follows:

• Scalars, Vectors, Matrices, Tensors
• Matrix Norms
• Special Matrices and Vectors
• Eigenvalues and Eigenvectors

2) Multivariate calculus

It helps in understanding the learning part of ML. It is what is used to learn from examples, update the parameters of different models and improve the performance.

It includes various topics as follows:

• Derivatives
• Integrals
• Gradients
• Differential Operators
• Convex Optimization

3) Probability Theory

Probability theory is one of the important concepts that help us to make assumptions about underlying data in deep learning and AI algorithms. It is important for us to understand the key probability concepts

It includes various topics as follows:

• Elements of Probability
• Random Variables
• Distributions
• Variance and Expectation
• Special Random Variables

Extra benefits:

Along with a certificate of completion, video lectures and online study materials, this course also includes projects and quizzes upon unlocking each section, which helps you to solidify your knowledge. Further, this course not only helps in building your own algorithms but also start putting your algorithms to use in your next projects.

Pre-requisites:

This course is designed for beginners as well as experienced levels. Further, basic knowledge of Python is needed as concepts are coded in Python and R.

Course rating: 4.5 out of 5

Course provider: Eduonix Learning Solutions, Eduonix-Tech

Course duration: 4.5 hours

Important link:Click here to enrol and know more about this course.

Conclusion

Mathematics is always a key player in entering the programming domain. All programming languages like Java, Python, R, Apex, C, etc., are required to have good mathematics knowledge to build your logical concepts and algorithms. In this topic, we have discussed a few important and best maths courses available online for learning Machine learning and AI solutions. Hopefully, after reading this article, you will be able to choose the best maths course to start your journey in ML and build your career in the IT world.

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