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Machine learning is a highly interdisciplinary topic and refers to a set of tools for modeling and understanding complex datasets. It is a field of computer science that uses statistical techniques to give computer systems the ability to \"learn\" with data without being explicitly programmed. These three-volume book series cover a wide variety of topics in machine learning, focusing only on supervised and unsupervised learning intended for those who want to become a data scientist or an expert in machine learning. The first volume covers the generalized linear models which are simpler, yet constitute the basics of machine learning. This book series shall be seen as a compilation of the thousands of information chips gathered from different materials and put together to form a concise description of this emerging field.

The book series takes the approach of building up first the basic concepts and then providing the mathematical framework to derive each machine learner step-by-step. It can be also seen as a very concise description of the algorithms included in scikit-learn library, but the primary goal is to provide a good understanding of not just the API of scikit-learn (and statsmodels), but also to give a good comprehension about what is under the hood, how they are working and what are the pros and cons of each machine learning algorithm. No single approach will perform well in all possible applications - as no universal machine learning algorithm exists that works well on all problems - thus, without understanding all the cogs and their interaction inside the machine (learner), it is impossible to select the best algorithm for a particular problem. Each chapter is accompanied by lab exercises stored in https://github.com/FerencFarkasPhD/Advanced-Machine-Learning as Jupyter Notebooks.

This book series is intended for both undergraduate and graduate students, as well as software developers, experimental scientists, engineers, and financial professionals with strong math backgrounds who wish to improve their machine learning skills. Thus, some mathematical background, equivalent to a one-semester undergraduate course, in each of the following fields is preferred: linear algebra, multivariate differential calculus, probability theory, and statistics. It is also assumed that the reader does have some sort of basic knowledge of computer science and possess knowledge of the basic computer skills and principles, including, but not limited to, data structures and algorithms. Basic programming skills, some knowledge of Python programming, the SciPy stack, and Jupyter Notebook is also required from the reader to carry out the lab exercises accompanying the book.

Although reading the three-volume series requires a solid math background, those who lack the necessary math skill should not run away in panic. The author is an engineer and not a mathematician who sees the math only as a tool that serves as a way to deepen the understanding of a problem at hand and to find the optimal solution for a practical problem. Thus, all mathematical formulas used by machine learning algorithms are introduced by formulating the background and providing additional intuition beforehand. With that in mind, minimal mathematical knowledge might be also acceptable for an eager learner to understand the book. Moreover, the mathematical expression for each machine learning algorithm is derived step by step with clear explanations, intuitive examples, and supporting figures.

For those not possessing a deep mathematical background - but have some programming knowledge - should see the vectors and matrices used in the mathematical framework as the counterpart of multidimensional arrays used in computer programs, while the mathematical formulas as a sequence of array manipulations. Thus, the mathematical formulas presented in the book will be converted directly into a single line of Python code using array manipulations. This approach is supported by both the Appendix and lab exercises.

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