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Authentic Guid to C Programming Language
Description: "Simplicity"- That has been the hallmark of this book in not only its previous fourteen English editions, but also in the Hindi, Guajarati, Japanese, Korean, Chinese and US editions. This book does not assume any programming background. It begins with the basics towards the end of the book. Each Chapter Contains: Lucid explanation of the concept Well thought-out, fully working programming examples End of chapter exercises that would help you practise the skills learned in the chapter Hand crafted "kanNotes” at the end of each chapter that would help the reader remember and revise the concepts covered in the chapter. Table of Contents : Getting Started C Instructions Decision Control Instruction More Complex Decision Making Loop Control Instruction More Complex Repetitions Case Control Instruction Functions Pointers Recursion Data Types Revisited The C Preprocessor Arrays Multidimensional Arrays Strings Handling Multiple Strings Structures Console Input/ Output File Input/ Output More Issues in Input/ Output Operations on Bits Miscellaneous Features C Under Linux Interview FAQ’s Appendix A- Compilation and Execution Appendix B- Precedence table Appendix C-Chasing the Bugs Appendix D- ASCII Chart Periodic Tests I to IV Index
Data Science from Scratch
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
