[FREE EBOOKS] Artificial Intelligence and Machine Learning Fundamentalss, Modern Python Cookbook,Third Edition & Four More Best Selling Titles
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 | Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances($25.99 Value) FREE for a Limited TimeMachine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore’s law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!. |
 | Modern Python Cookbook,Third Edition ($43.99 Value) FREE for a Limited TimePython is the go-to language for developers, engineers, data scientists, and hobbyists worldwide. Known for its versatility, Python can efficiently power applications, offering remarkable speed, safety, and scalability. This book distills Python into a collection of straightforward recipes, providing insights into specific language features within various contexts, making it an indispensable resource for mastering Python and using it to handle real-world use cases. The third edition of Modern Python Cookbook provides an in-depth look into Python 3.12, offering more than 140 new and updated recipes that cater to both beginners and experienced developers. This edition introduces new chapters on documentation and style, data visualization with Matplotlib and Pyplot, and advanced dependency management techniques using tools like Poetry and Anaconda. With practical examples and detailed explanations, this cookbook helps developers solve real-world problems, optimize their code, and get up to date with the latest Python features. |
 | Marketing with AI For Dummies ($18.00 Value) FREE for a Limited TimeStay ahead in the marketing game by harnessing the power of artificial intelligence. Marketing with AI For Dummiesis your introduction to the revolution that’s occurring in the marketing industry, thanks to artificial intelligence tools that can create text, images, audio, video, websites, and beyond. This book captures the insight of leading marketing executive Shiv Singh on how AI will change marketing, helping new and experienced marketers tackle AI marketing plans, content, creative assets, and localized campaigns. You’ll also learn to manage SEO and customer personalization with powerful new technologies. Peek at the inner workings of AI marketing tools to see how you can best leverage their capabilities. Identify customers, create content, customize outreach, and personalize customer experience with AI. Consider how your team, department, or organization can be retooled to thrive in an AI-enabled world. Learn from valuable case studies that show how large organizations are using AI in their campaigns. This easy-to-understand Dummies guide is perfect for marketers at all levels, as well as those who only wear a marketing hat occasionally. Whatever your professional background,Marketing with AI For Dummieswill usher you into the future of marketing. |
 | How to Become a Data Analyst ($15.00 Value) FREE for a Limited TimeStart a brand-new career in data analytics with no-nonsense advice from a self-taught data analytics consultant. InHow to Become a Data Analyst: My Low-Cost, No Code Roadmap for Breaking into Tech, data analyst and analytics consultant Annie Nelson walks you through how she took the reins and made a dramatic career change to unlock new levels of career fulfilment and enjoyment. In the book, she talks about the adaptability, curiosity, and persistence you’ll need to break free from the 9-5 grind and how data analytics-with its wide variety of skills, roles, and options-is the perfect field for people looking to refresh their careers. Annie offers practical and approachable data portfolio-building advice to help you create one that’s manageable for an entry-level professional but will still catch the eye of employers and clients. You’ll also find: Deep dives into the learning journey required to step into a data analytics role. Ways to avoid getting lost in the maze of online courses and certifications you can find online-while still obtaining the skills you need to be competitive. Explorations of the highs and lows of Annie’s career-change journey and job search-including what was hard, what was easy, what worked well, and what didn’t. Strategies for using ChatGPT to help you in your job search. A must-read roadmap to a brand-new and exciting career in data analytics,How to Becomea Data Analystis the hands-on tutorial that shows you exactly how to succeed. |
 | Machine Learning: Hands-On for Developers and Technical Professionals, 2nd Edition ($32.00 Value) FREE for a Limited TimeDig deep into the data with a hands-on guide to machine learning with updated examples and more!. Machine Learning: Hands-On for Developers and Technical Professionalsprovides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor’s Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data.Machine Learningis an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka. Understand decision trees, Bayesian networks, and artificial neural networks. Implement Association Rule, Real Time, and Batch learning. Develop a strategic plan for safe, effective, and efficient machine learning. By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science,Machine Learning: Hands-On for Developers and Technical Professionalsprovides the skills and techniques required to dig deeper. |
 | Deep Learning: A Practical Introduction ($88.00 Value) FREE for a Limited TimeAn engaging and accessible introduction to deep learning perfect for students and professionals. InDeep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools. Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures. Practical discussions of recurrent neural networks and non-supervised approaches to deep learning. Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks. Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks,Deep Learning: A Practical Introductionwill also benefit practitioners and researchers in the fields of deep learning and machine learning in general. |
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