Software Engineering

Software Engineering (8)


    By Ian Sommerville
    The book is primarily aimed at university and college students taking introductory and advanced courses in software and systems engineering. Software engineers in the industry may find the book useful as general reading and as a means of updating their knowledge on topics such as software reuse, architectural design, dependability and security, and process improvement.

  • Software Engineering for Students (Fourth Edition)

    This book is for people who have experienced the pleasures of writing programs and who want to see how things change in the scale up to large programs and software systems. This book provides an introduction to software engineering for students in undergraduate programs in Computer Science, Computer Studies, Information Technology, Software Engineering and related fields at the college or university level.


    By Rod Stephens
    This book describes software engineering. It explains what software engineering is and how it helps produce applications that are effective, fl exible, and robust enough for use in real‐world situations. This book won’t make you an expert systems analyst, software architect, project manager, or programmer, but it explains what those people do and why they are necessary for producing high‐quality software.

  • Software Engineering Tutorial

    By Tutorials Point
    This tutorial provides you the basic understanding of software product, software design and development process, software project management and design complexities. At the end of the tutorial you should be equipped with well understanding of software engineering concepts.

  • The New Software Engineering

    By Sue Conger
    The goal of this book, then, is to discuss project planning, project life cycles, methodologies, technologies, techniques, tools, languages, testing, ancillary technologies (e.g., database), and computer-aided software engineering (CASE). For each topic, alternatives, benefits and disadvantages are discussed.

  • Software Engineering Best Practices

    The primary goal of this book on software engineering best practices is to provide incentive for putting software engineering on a solid basis of facts derived from accurate measurement of quality and productivity.

  • Introduction to Software Engineering

    By Dr. Rakesh Kumar
    The objective of this lesson is to make the students acquainted with the introductory concepts of software engineering. To make them familiar with the problem of software crisis this has ultimately resulted into the development of software engineering.

  • Machine Learning in Action

    By Peter Harrington
    This book sets out to introduce people to important machine learning algorithms. Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning books is available, which discuss the mathematics, but discuss little of how to program the algorithms.

Machine Learning (6)

  • Machine Learning in Python®:Essential Techniques for Predictive Analysis

    By Michael Bowles
    This book is intended for Python programmers who want to add machine learning to their repertoire, either for a specific project or as part of keeping their toolkit relevant. Perhaps a new problem has come up at work that requires machine learning. With machine learning being covered so much in the news these days, it’s a useful skill to claim on a resume.

  • Machine Learning For Dummies®, IBM Limited Edition

    By Judith Hurwitz and Daniel Kirsch
    Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights

  • A Course in Machine Learning

    By Hal Daumé III
    The purpose of this book is to provide a gentle and pedagogically organized introduction to the field. as the list of dependencies at the end of this chapter. The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. (A little bit of linear algebra and probability will not hurt.)

  • Understanding Machine Learning: From Theory to Algorithms

    By Shai Shalev-Shwartz and Shai Ben-David
    The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks


    By Christopher M. Bishop
    This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts