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Best Mathematics books for Machine Learning, Data Science

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Best Mathematics Books for Machine Learning
Best Mathematics Books for Machine Learning

Are you looking for the best books on Mathematics for Machine Learning?… If yes, then this article is for you. In this article, you will find the Best Mathematics Books for Machine Learning & advanced like Beginner courses, and Practice test coursesSo, check these Best Mathematics Books for Machine Learning and find the Best Mathematics Books for Machine Learning for Beginners to Advanced according to your need.

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Best Mathematics Books for Machine Learning for Beginners to Advanced to know in 2022

Mathematics for Machine Learning

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics.

This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.

For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book’s web site.

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Machine Learning: An Applied Mathematics Introduction

fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the most important techniques

  1. Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)
  2. General Matters (In one chapter all of the mathematical concepts you’ll need to know. From jargon and notation to maximum likelihood, from information theory and entropy to bias and variance, from cost functions to confusion matrices, and more)
  3. K Nearest Neighbours 
  4. K Means Clustering
  5. Naïve Bayes Classifier
  6. Regression Methods
  7. Support Vector Machines
  8. Self-Organizing Maps
  9. Decision Trees
  10. Neural Networks
  11. Reinforcement Learning

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Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.

You’ll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks.

Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works.

As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.

  • Understand the key mathematical concepts for building neural network models
  • Discover core multivariable calculus concepts
  • Improve the performance of deep learning models using optimization techniques
  • Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer
  • Understand computational graphs and their importance in DL
  • Explore the backpropagation algorithm to reduce output error
  • Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)

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Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you’ll also gain practical insights into the state of data science and how to use those insights to maximize your career.

  • Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
  • Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
  • Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
  • Manipulate vectors and matrices and perform matrix decomposition
  • Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
  • Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

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Linear Algebra and Optimization for Machine Learning: A Textbook

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:

Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.

Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields.  Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. 

A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

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Practical Discrete Mathematics: Discover math principles that fuel algorithms for computer science and machine learning with Python

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks.

Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level.

As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science.

  • Understand the terminology and methods in discrete math and their usage in algorithms and data problems
  • Use Boolean algebra in formal logic and elementary control structures
  • Implement combinatorics to measure computational complexity and manage memory allocation
  • Use random variables, calculate descriptive statistics, and find average-case computational complexity
  • Solve graph problems involved in routing, pathfinding, and graph searches, such as depth-first search
  • Perform ML tasks such as data visualization, regression, and dimensionality reduction

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Unraveling the Mathematics of Machine Learning and Deep Learning Algorithms: Demystifying Basic to Advanced Concepts & Mathematics of Machine Learning and Deep Learning

Artificial Intelligence (AI) has grown to be a very popular field in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans.

Machine learning is a branch of artificial intelligence based on the idea that computers can learn from data, identify patterns and make decisions with minimal human intervention and gradually improve in decision making over time with more data.

Deep learning is a subset of machine learning and is one of the most popular fields in artificial intelligence (AI). This book entails the machine learning and deep learning concepts, basic to advanced algorithms along with the explanation of mathematics behind each algorithm.

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And here the list ends. So, these are the Best Mathematics Books for Machine Learning for Beginners to Advanced. I will keep adding more Best Mathematics Books for Machine Learning for Beginners to advance to this list.

Conclusion

I hope these Best Mathematics Books for Machine Learning for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.

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