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via Udemy |
Go to Course: https://www.udemy.com/course/machine-learning-2020-complete-maths-for-machine-learning/
Certainly! Here's a comprehensive review and recommendation for the "Mathematics for Machine Learning" course on Coursera: --- **Course Review and Recommendation: Mathematics for Machine Learning** If you're venturing into the world of Data Science and Machine Learning, mastering the underlying mathematics is essential, and this course on Coursera is an excellent starting point. Titled "Mathematics for Machine Learning," it bridges the gap between theoretical math concepts and their real-world applications in machine learning algorithms. **Course Overview** This course provides a solid foundation in key mathematical topics, including Algebra, Calculus, Linear Algebra, and Probability—crucial for understanding how machine learning models work under the hood. The instructor emphasizes an intuitive approach, deriving formulas and concepts step-by-step to help learners grasp both the "how" and "why" of each topic. The course is divided into four main sections: - **Algebra Foundations:** Covers equations, functions, logs, and polynomials, essential for understanding data transformations and loss functions. - **Calculus:** Focuses on derivatives, gradients, and optimization techniques like Gradient Descent—central to training machine learning models. - **Linear Algebra:** Delves into vectors, matrices, and their operations, which are fundamental when working with large datasets and implementing algorithms. - **Probability:** Explains probability distributions, conditional probability, and their applications in classification and statistical inference. **Strengths** - **Clear, Intuitive Teaching:** The instructor's passion for mathematics shines through, making complex topics accessible and engaging. - **Real-World Relevance:** Each mathematical concept is tied back to machine learning applications, helping learners see the practical significance. - **Comprehensive Content:** The coverage of topics is well-rounded, providing a strong base for anyone aiming to deepen their understanding of Data Science and Machine Learning. - **Suitable for All Levels:** Whether you just want to refresh your math skills or need a detailed introduction, this course offers value. **Who Should Enroll?** - Beginners with some prior exposure to basic math looking to understand the fundamentals of machine learning. - Data scientists and aspiring ML engineers who want to strengthen their mathematical foundation. - Anyone interested in the "why" behind machine learning algorithms, not just the implementation. **Final Verdict** This course is highly recommended for anyone serious about excelling in Data Science or Machine Learning. It demystifies complex mathematical concepts, making them accessible and applicable. The instructor’s enthusiasm adds an extra layer of motivation to learn and understand deeply. **Recommendation** Enroll in this course if you aspire to be proficient in machine learning and data analysis. It’s a wise investment that will pay off by giving you the mathematical clarity needed to build, troubleshoot, and optimize models effectively. With a solid grasp of these mathematical principles, you'll be better equipped to innovate and succeed in the fast-evolving field of data science. --- Hope this helps! Let me know if you'd like a shorter version or further details.
Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Mathematics form the basis of almost all the Machine Learning algorithms. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it. You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you can not correlate them with Machine Learning. This course of Math For Machine Learning, aims to bridge that gap. We will get you upto speed in the mathematics required for Machine Learning and Data Science. We will go through all the relevant concepts in great detail, derive various formulas and equations intuitively. This course is divided into following sections,Algebra FoundationsIn this section, we will lay the very foundation of Algebraic Equations including Linear Equations and how to plot them. We will understand what are Exponents, Logs, Polynomial and quadratic equations. Almost all the Machine Learning algorithms use various functions for loss measurement or optimization. We will go through the basics of functions, how to represent them and what are continuous and non-continuous functions.CalculusIt is said that without calculus and differential equations, Machine Learning would have never been possible. The Gradient Descent using derivatives is essence of minimizing errors for a Machine Learning algorithm. We will understand various terms of Rate of Change, Limits, What is Derivative, including Single, Double and Partial Derivatives. I will also explain with an example, how machine learning algorithms use calculus for optimization.Linear AlgebraLinear Algebra is the mathematics of the 21st Century. Every record of data is bound by some form of algebraic equation. However, it's nearly impossible for humans to create such an equation from a dataset of thousands of records. That's where the ability of vectors and matrices to crunch those numerical equations and create meaningful insights in the form of linear equations help us. We will see, right from the foundations of Vectors, Vector Arithmetic, Matrices and various arithmetic operations on them. We will also see, how the vectors and matrices together can be used for various data transformations in Machine Learning and Data Science.ProbabilityProbability plays an important role during classification type of machine learning problems. It is also the most important technique to understand the statistical distribution of the data. Conditional probability also helps in classification of the dependent variable or prediction of a class. With all of that covered, you will start getting every mathematical term that is taught in any of the machine learning and data science class.Mathematics has been my favorite subject since the childhood and you will see my passion in teaching maths as you go through the course. I firmly believe in what Einstein said, "If you can not explain it simple enough, You have not understood it enough.". I hope I can live upto this statement.I am super excited to see you inside the class. So hit the ENROLL button and I will see you inside the course.You will truly enjoy Mathematics For Machine Learning....