Just Enough Data Science And Machine Learning
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Just Enough Data Science and Machine Learning
Author: Mark Levene
language: en
Publisher: Addison-Wesley Professional
Release Date: 2024-12-04
An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and concepts Coverage of various types of data and techniques for analysis In-depth exploration of popular machine learning tools and methods Insight into specific data science topics, such as social networks and sentiment analysis Practical examples and case studies for real-world application Recommended further reading for deeper exploration of specific topics.
Essential Math for Data Science
Author: Thomas Nield
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-05-26
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. Learn how to: 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
Technology vs People
Author: Michael de Kare-Silver
language: en
Publisher: Troubador Publishing Ltd
Release Date: 2024-07-28
As machine software develops, it becomes more and more intelligent. More capable of doing things that we humans have been used to doing, have assumed that that is our job, our role, our responsibility. As this Ai Tech age advances, so the world is being faced by a challenge: can Technology and People continue to live harmoniously together, a world where the Tech supports, enables and complements what People can do? Or will the machines take over? Such is the pace of technology-driven change that companies around the world are scrambling to catch-up, to transform, reinvent themselves for this Digital Tech age. FinTech, InsurTech, Blockchain, Bitcoin, Cloud, Artificial Intelligence, Machine Learning, Virtual Reality, Robotics, Cyber Security, Internet of Things…there’s seems no end to what new Tech is generating and with it the substantial challenges, and opportunities, for every organisation. This new book sets out a possible roadmap and blueprint to help companies navigate their way through these changing times. It looks at best practices and lessons learned and aims to distil that into a clear set of guidelines and working advice.