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Python & AI For Dummies
Unlock the combined power of Python and AI to supercharge your next technical project and your career Python & AI For Dummies explores the exciting combo of two revolutionary technologies that are reshaping how we work, code, and solve problems. Authors John C. Shovic and Mary Everett draw on their extensive work in computer science, precision agriculture, and artificial intelligence to walk you through the intersection of Python programming and AI applications. No matter your professional background, learn how to use Python's versatility to unlock AI's transformative power. This guide offers hands-on approaches to implementing AI with Python. It moves from foundational concepts to advanced applications, covering essential Python libraries: TensorFlow, PyTorch, OpenCV, and scikit-learn. It also explores essential AI concepts, like neural networks, machine learning models, and natural language processing. Python & AI For Dummies is packed with hands-on projects and real-world examples, that explain how to build machine vision systems, create language models, implement reinforcement learning, and develop AI agents. Broken down into manageable, jargon-free lessons, explore topics from theoretical programming fundamentals to cutting-edge AI applications. Inside the book: Grasp fundamental AI concepts including machine learning, neural networks, and deep learning architecturesExplore eight essential Python libraries designed specifically for AI development and data manipulationBuild practical projects, including machine vision systems, large language models, and AI agentsLearn proven techniques for data preprocessing, model training, and avoiding common pitfalls like overfittingGain insights into emerging technologies including generative AI, transformers, and the Model Context Protocol A can’t-miss resource for programmers interested in expanding their AI skillset, professionals in finance or marketing who want to develop new capabilities, and small business owners ready to streamline their operations, Python & AI For Dummies offers step-by-step guidance, troubleshooting tips, and real-world applications in industries from manufacturing to agriculture.
R 750.00
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R 740.48
Artificial Intelligence with Python
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book• Step into the amazing world of intelligent apps using this comprehensive guide• Enter the world of Artificial Intelligence, explore it, and create your own applications• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.What You Will Learn• Realize different classification and regression techniques• Understand the concept of clustering and how to use it to automatically segment data• See how to build an intelligent recommender system• Understand logic programming and how to use it• Build automatic speech recognition systems• Understand the basics of heuristic search and genetic programming• Develop games using Artificial Intelligence• Learn how reinforcement learning works• Discover how to build intelligent applications centered on images, text, and time series data• See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
R 1,889.00
R 580.00
Artificial Intelligence with Python
New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook DescriptionArtificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is forThe intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
R 1,889.00
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
R 1,482.00
Beginning ChatGPT for Python
Unlock the future of software development and empower yourself to elevate your Python applications by harnessing the power of AI as this field continues to grow and evolve. Perfect for beginner to intermediate Python programmers, this book breaks down the essentials of using ChatGPT and OpenAI APIs.You'll start with the basics, learning to authenticate, send prompts, generate responses, test in the Playground, and handle errors with ease. Each chapter includes hands-on exercises that bring concepts to life, demonstrating different API functionalities and practical applications. You'll master models like GPT-4o, GPT-4, GPT-3.5, Whisper, and DALL-E, enabling you to enhance your applications with cutting-edge AI.Discover how generative AI tools like ChatGPT can automate tedious tasks rather than replace jobs. Leverage ChatGPT’s powerful Natural Language Processing (NLP) capabilities to handle various formats of unstructured text within your Python apps. Quickly see how easy it is to use ChatGPT as your AI-pair programmer, boosting your productivity and speed.This step-by-step guide will have you creating intelligent chatbots that can automatically process messages from Slack or Discord. With Beginning ChatGPT for Python, you'll master the ChatGPT and OpenAI APIs, building intelligent applications that offer a personalized and engaging user experience.What You’ll LearnConnect with the ChatGPT and OpenAI APIs and send effective prompts.Harness parameters like temperature and topp to create unique and engaging responses from ChatGPT.Create an intelligent assistant bot for Slack that automates tasks and enhances productivity.Develop a bot that can moderate conversations and manage communities on Discord.Add context to your prompts to get more accurate and relevant responses. Who This Book Is For Python developers and enthusiasts who aspire to employ OpenAI and ChatGPT in the creation of intelligent applications to enhance productivity.
R 1,368.00
R 390.00
Applied Reinforcement Learning with Python
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.What You'll LearnImplement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
R 1,079.73
Practical Explainable AI Using Python
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.What You'll LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
R 1,596.00
R 1,005.00
Using Stable Diffusion with Python
Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design Key FeaturesMaster the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python codeGet instant access to emerging extensions and open-source modelsLeverage the power of community-shared models and LoRA to produce high-quality images that captivate audiencesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionStable Diffusion is a game-changing AI tool for image generation, enabling you to create stunning artwork with code. However, mastering it requires an understanding of the underlying concepts and techniques. This book guides you through unlocking the full potential of Stable Diffusion with Python.Starting with an introduction to Stable Diffusion, you'll explore the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll learn how to optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. After covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion application. This book also delves into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction.By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.What you will learnExplore core concepts and applications of Stable Diffusion and set up your environment for successRefine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversionHarness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and qualityExplore developments in Stable Diffusion such as video generation using AnimateDiffWrite effective prompts and leverage LLMs to automate the processDiscover how to train a Stable Diffusion LoRA from scratchWho this book is forIf you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.
R 1,531.00
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R 3,289.00
AI Projects in PyTorch
Dive into computer vision, natural language processing, and recommender systems by building end-to-end projects in PyTorch — one of the most widely used deep learning frameworks among researchers and engineers worldwide. This book takes you from the fundamentals to complete, hands-on projects, giving you the confidence to start creating your own AI solutions.The book begins with a chapter on the fundamentals of machine learning, laying the groundwork by introducing key aspects of an ML project such as data preprocessing, feature engineering, model training, and evaluation, along with essential concepts like overfitting and underfitting. The following chapter, "Tensors in PyTorch," explores data handling in PyTorch -- from basic tensor operations to advanced gradient computations -- providing a deeper understanding of data transformations.With the foundations in place, the book moves on to hands-on projects. Chapter 3 introduces you to the world of computer vision, where you will build an image classifier using convolutional neural networks. The next three chapters immerse you in natural language processing: beginning with text classification (Chapter 4), tackling a range of NLP tasks with Hugging Face (Chapter 5), and culminating in the creation of a storytelling language model (Chapter 6).The focus then shifts to other key AI domains – you will tackle an audio classification task (Chapter 7), build a recommender system in PyTorch (Chapter 8), and finish with a multi-modal project that combines computer vision and natural language processing to build an image captioning system (Chapter 9).Whether you're a software engineer looking to break into the world of AI or a beginner with basic Python skills, "AI Projects with PyTorch" offers practical guidance and hands-on experience to start building your own AI applications with confidence.What you will learn:Master the core principles of machine learning and gain confidence with the typical PyTorch project workflow.Build a solid understanding of data handling in PyTorch – including tensors, datasets, data loaders, and gradient computations.Build natural language processing models, from text classification to storytelling language models.Work on multiple natural language processing tasks with Hugging Face libraries.Combine vision and language to build an image captioning system. Who this is book is for:- Python programmers and software engineers who are new to AI and want a practical, project-based introduction with PyTorch. ML engineers who wish to expand into other AI domains such as computer vision, natural language processing, audio processing, etc.
R 1,254.00
R 1,069.00
