As software and programming libraries improve, most Machine learning practitioners aren’t aware of the low-level technical details of the algorithms. Hence, there is a danger that a practitioner becomes unaware of the design decisions and, hence, the limits of machine learning algorithms.
The second part will focus on the four pillars of Machine Learning after defining the three components of machine learning (data, models, and parameter estimation) mathematically. These four pillars are:
The book is meant to be a Guidebook in the mathematics that forms the foundation of Machine Learning. They propose an understanding of Machine Learning as a science that is built upon as follows:
I am not going to lie, this is a dense and detailed book. You will have to invest time and effort to go through it and deeply understand its topics. But I assure you that it will be worth it. Do not give up, take your time, and make sure that you internalize its lessons. It will definitely pay up greatly in your understanding and application of Machine Learning.
Dimensionality Reduction: using principal component analysis, the goal will be to find a lower-dimensional representation of the input data. This will allow an easier analysis. It is important to notice that in these methods there arent target values. Dimensionality reduction belongs to the set of techniques of the so-called Unsupervised Learning.
Vector Calculus: That will allow the understanding of the optimization techniques that are used to find the parameters that will maximize (or minimize) some performance measure, such as Gradient Descent.
Classification: Similarly to regression, classification also belongs to Supervised Learning, and it is studied through the lens of Support Vector Machines. Unlike regression, target values are typically integers, instead of real values.
Matrix Decomposition: To analyze operations on matrices that are extremely useful in Machine Learning as they enable the Data Scientist to build an intuitive representation of the data, its transformations, and how to perform efficient learning.
After reading/studying it for the first time, it is a great tool to have at the side and I encourage you to come back to it and refresh the related concepts every time that you face a Machine Learning challenge. It will give you a great perspective on how to tackle blocking points and definitely ease up your path in the mid-long term.
This great book was written by Marc Peter Deisenroth, A.Aldo Faisal, and Chen Soon Ong and published on the 23rd of April 2020 and it will lay you everything you need to successfully understand and apply most of Machine Learning and Deep Learning algorithms, optimization mechanisms, cost functions… in sort all you will use throughout your Data Science Career.
Linear Regression: where the goal will be to find the function that will map the input to a corresponding target value, which will typically be a real number. Topics covered are model fitting by parameter estimation (linear regression) and by parameter integration (Bayesian regression).
You can also make 200 word posts your warm up. I write long-form blog posts two days a week. Sometimes I’ll use this process as a warm-up to get my brain into a flow state, and to get my fingers typing. You can write emails, post comments online, or text a friend as a writer’s warm-up too.
I personally believe that the best value that brings this book is that it links greatly the mathematical concepts explained in the first part of the book to the Machine Learning algorithms detailed in the second part. If you ever struggled understanding concepts like Gradient Descent, you won’t need to worry anymore about it after studying Mathematics for Machine Learning.
Density Estimation: The objective will be to find a probability distribution that describes the input data. The focus will be on Gaussian Mixture models to do this and it also belongs to Unsupervised Learning.
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