Review — Is Mathematics for Machine Learning and Data Science Specialization on Coursera worth it?
Maths is needed for Machine Learning and Data Science and this Course will teach you that.
Hello guys, if you are looking for a Coursera resource to learn Probability and Mathematics then you have come to the right place. Earlier, I have shared best Mathematics and Statistics courses for Data Scientists and Machine Learning engineer and in this article, I am going to review, one of the newest and comprehensive Maths course by DeepLearning.AI.
If you’re interested in pursuing a career in machine learning or data science, you’ll undoubtedly need a strong foundation in math to succeed. Math for Machine Learning by DeepLearning.AI course promises to provide just that — a comprehensive review of the mathematical concepts needed for machine learning.
In this review, we’ll take a closer look at the course, its structure, and the skills you can expect to gain from it. Read on to discover whether Math for Machine Learning is worth your time and investment or not.
Mathematics for Machine Learning and Data Science is a comprehensive online program designed to teach learners the mathematical concepts and techniques necessary for success in the fields of machine learning and data science.
The specialization is composed of three courses: Linear Algebra, Calculus, and Probability and Statistics for Data Science. Created by DeepLearning.AI, a data science education company by Andrew Ng, founder of Coursera, and taught by Luis Serrano, this program uses innovative pedagogy to make learning quick, intuitive, and easy to follow.
Why Data Scientist should Learn Maths?
Mathematics is a fundamental and essential tool for data scientists and if you want to start a career in Data Science you must learn Mathes, in particularly Probability, Statistics, and Calculus. Here are some reasons why data scientists should learn maths:
Understanding algorithms
Many machine learning algorithms, such as linear regression, decision trees, and neural networks, rely heavily on mathematical concepts such as calculus, linear algebra, and probability theory. By having a strong foundation in maths, data scientists can better understand how these algorithms work and how to apply them effectively.
Data cleaning and preprocessing
Data scientists need to clean and preprocess data before they can use it for analysis. This involves mathematical techniques such as scaling, normalization, and imputation. Having a good knowledge of Mathematics concepts will help you to clean and pre-process data effectively.
Statistical analysis:
Data scientists often use statistical analysis to draw insights from data. This involves mathematical concepts such as hypothesis testing, regression analysis, and probability distributions.
Data visualization:
Data scientists also need to create visualizations to help communicate their findings. This involves understanding concepts such as coordinate systems, color theory, and visualization techniques.
Model evaluation
Data scientists also need to evaluate the accuracy and performance of their models. This involves using mathematical techniques such as cross-validation and scoring metrics.
Advanced techniques
As data science becomes more advanced, techniques such as deep learning and natural language processing require a strong foundation in calculus, linear algebra, and other advanced mathematical concepts.
In short, Mathematics is an essential tool for data scientists to understand and apply machine learning algorithms, clean and preprocess data, perform statistical analysis, create effective visualizations, evaluate models, and work with advanced techniques. A strong foundation in maths is crucial for success in data science and Mathematics for Machine Learning and Data Science will help you to learn that.
Is Mathematics for Machine Learning and Data Science good for Beginners?
Whether you’re a beginner or an experienced data scientist, this specialization will equip you with the fundamental mathematics toolkit of machine learning.
The course content is presented in a clear and accessible manner, with easy-to-follow plugins and visualizations that help learners see how the math behind machine learning actually works.
The specialization is designed for learners with at least a high school mathematics background and a basic familiarity with Python. Throughout the program, you will gain a deep understanding of the math that makes machine learning algorithms work.
You will also develop statistical techniques that will empower you to get more out of their data analysis and acquire the fundamental skills that employers desire, helping them ace machine learning interview questions and land their dream job.
After completing this specialization, you will be able to represent data as vectors and matrices, apply common vector and matrix algebra operations like dot product, inverse, and determinants, and express certain types of matrix operations as linear transformations.
You will also be able to apply concepts of eigenvalues and eigenvectors to machine learning problems, optimize different types of functions commonly used in machine learning, and perform gradient descent in neural networks with different activation and cost functions.
Additionally, you will be able to describe and quantify the uncertainty inherent in predictions made by machine learning models, understand the properties of commonly used probability distributions in machine learning and data science, apply common statistical methods like MLE and MAP, and assess the performance of machine learning models using interval estimates and margin of errors.
Overall, if you’re looking to gain a deep understanding of the mathematics behind machine learning and data science Mathematics for Machine Learning and Data Science Specialization is an excellent specialization that is definitely worth your time and investment.
Course Review
Now that we know what are you going to learn on this specialization, its time to deep dive into each of the course and find out what are the key skill you are going to learn in each course and in how much depth.
1. Linear Algebra for Machine Learning and Data Science
Linear Algebra for Machine Learning and Data Science is an excellent course created by DeepLearning.AI and taught by Luis Serrano. This course covers a range of topics, from representing data as vectors and matrices to identifying their properties using concepts of singularity, rank, and linear independence.
You will also gain experience with common vector and matrix algebra operations like dot product, inverse, and determinants, and they will be able to express certain types of matrix operations as linear transformations.
Additionally, the course covers concepts of eigenvalues and eigenvectors, which are crucial to understanding machine learning problems.
After completing the course, you will have a deep understanding of linear algebra concepts as they relate to machine learning, and they’ll be ready to tackle more advanced courses in the Mathematics for Machine Learning and Data Science program.
Overall, Linear Algebra for Machine Learning and Data Science is a highly recommended course for anyone looking to strengthen their math skills in the field of machine learning.
Here is the link to join this course — Linear Algebra for Machine Learning and Data Science
2. Calculus for Machine Learning and Data Science
Calculus for Machine Learning and Data Science is a course offered by DeepLearning.AI and taught by Professor Serrano. This course is part of the Mathematics for Machine Learning and Data Science Specialization and is designed to teach learners the fundamental concepts of calculus that underlie many machine learning algorithms.
Here are key things you will learn in this course:
- key concepts of limits, derivatives, and integrals
- calculus to solve optimization problems in machine learning
- How to use chain rule and gradient descent algorithms
- Understand how calculus is used in neural networks and deep learning
The course is structured to provide a strong foundation in the concepts of calculus, including both theory and practice. The material is presented in an engaging and accessible way, with clear explanations and visual aids to help learners grasp the concepts.
One of the best thing about the course is the practical application of calculus to real-world problems in machine learning. This makes the concepts more tangible and easier to understand for learners who may not have a background in calculus.
Overall, Calculus for Machine Learning and Data Science is an excellent course for anyone looking to deepen their understanding of the mathematical concepts underlying machine learning. The course is well-structured, the instructor is knowledgeable and engaging, and the content is both accessible and practical.
Here is the link to join this course — Calculus for Machine Learning and Data Science
2. Probability & Statistics for Machine Learning & Data Science
Probability & Statistics for Machine Learning & Data Science is is the third and a beginner-friendly course offered by DeepLearning.ai, where learners can master the fundamental mathematics toolkit required for machine learning.
This course focuses on concepts of probability, random variables, and probability distributions, along with their application in machine learning problems.
The course is designed to help learners visually and intuitively understand the properties of commonly used probability distributions, such as Bernoulli, Binomial, and Gaussian distributions. It also covers common statistical methods, including maximum likelihood estimation and maximum a priori estimation, and statistical hypothesis testing in data science, such as AB testing.
Overall, the course is well-structured and provides learners with an easy-to-follow approach to learning. The instructor’s teaching style is also engaging, and the visualizations and plugins used throughout the course help learners understand how the math behind machine learning actually works.
Here is the link to join this course- Probability & Statistics for Machine Learning & Data Science
This course is highly recommended for anyone looking to enhance their understanding of probability and statistics and apply these concepts to their machine learning career.
The recommended background for this course includes at least high school-level mathematics, and a basic familiarity with Python, as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.
People’s Review
DeepLearning.AI has a good reputation in the field of machine learning education, and their courses are highly regarded by many in the industry. The specialization is also taught by experienced instructors who are experts in their respective fields.
Too easy for exercise, but the video lession is good, focused on ML perspective
Conclusion
In conclusion, the “Mathematics for Machine Learning and Data Science” specialization on Coursera is definitely worth it. The program is well-structured, with three courses that cover the foundational mathematical concepts and techniques that are crucial to understanding machine learning algorithms.
The courses are taught by Luis Serrano, an experienced instructor who uses innovative pedagogy to teach math concepts intuitively.
The content of the courses is also presented in a beginner-friendly manner, with easy-to-follow plugins and visualizations that help learners see how math applies to machine learning.
The specialization also covers a wide range of mathematical concepts and techniques, including linear algebra, calculus, probability, and statistics, all of which are essential for success in machine learning and data science.
Overall, I highly recommend this specialization to anyone looking to build a strong foundation in mathematics for machine learning and data science. The quality of the structure, instructor, and content make this program an excellent investment of time and effort.
By the way, If you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It costs around $59/ per month but is worth it because you get access to more than 7000+ courses and projects, and you can also get unlimited certificates.
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