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SOURCE: Krishna H. Mastering AI. From Algorithms to Applications. AI Models Built...2024
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COVER
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MEDIAINFO
Textbook in PDF format Unlock the Power of Artificial Intelligence. In a world where AI is rapidly transforming industries and reshaping our daily lives, understanding the fundamentals of Artificial Intelligence (AI) has never been more important. Mastering AI is your essential guide to navigating the world of AI—from its foundational concepts to its cutting-edge applications. This book breaks down complex topics like Machine Learning, neural networks, and natural language processing (NLP) into clear, accessible language. Whether you’re a business leader looking to leverage AI for growth, a tech enthusiast, or a curious learner. AI has the ability to decrease costs, improve productivity, and increase revenue. These benefits do however come with certain risks. In order to understand and harness AI’s full potential, this book will give you the knowledge and tools you need to make sense of the AI revolution. KNIME, or Konstanz Information Miner, is a free, open-source platform for Data Science that allows users to perform data analytics, reporting, and integration. It is designed to be easy to learn, but still capable of performing complex analyses. KNIME integrates various components for Machine Learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with minimal, programming. KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, using interactive widgets and views. KNIME is written in Java and based on Eclipse. It makes use of an extension mechanism to add plugins providing additional functionality. PyTorch is an open-source machine-learning framework built upon the Torch library. It supports a wide range of applications, including computer vision and natural language processing. It is celebrated for its adaptability and capacity to dynamically manage computational graphs. Key Features: Dynamic computation graph that allows for flexibility in model architecture. Staunch support for deep learning and neural networks. Large ecosystem of tools and libraries. LangChain is an open-source framework for building applications based on large language models (LLMs). These are large deep-learning models pre-trained on substantial amounts of data that can generate responses to user queries—for example, answering questions or creating images from text-based prompts. LangChain provides tools and abstractions to improve the customization, accuracy, and relevancy of the information the models generate. For example, developers can use LangChain components to build new prompt chains or customize existing templates. LangChain also includes components that allow LLMs to access new data sets without retraining. Inside Mastering AI, you’ll explore: Core AI Concepts: From algorithms to automation, learn the science behind AI. Real-World Applications: Discover how AI is driving innovation in healthcare, finance, manufacturing, and more. Ethical Considerations: Engage with the critical debates about AI's impact on privacy, jobs, and society. The Future of AI: What’s next for artificial intelligence, and how can we shape its direction? Written for readers of all backgrounds, Mastering AI is both a practical guide and a thought-provoking exploration of AI’s potential. Whether you’re looking to integrate AI into your work or simply understand its role in the modern world, this book will inspire and inform you. In-depth understanding of Gen AI and open LLM concepts Build your 1st AI model using Knime an AI/ML tool. Real Case studies step by step walk thru on AI Model building Do it yourself Exercise Quiz to test your knowledge Key take aways at the end of each chapter AI Introduction Introduction to ML Tools Exploratory Data Analysis Regression Classification Neural Networks Ensemble Techniques Clustering Dimensionality Reduction NLP application – Sentimental Analysis Recommendation Systems AI Computer Vision Generative AI AI Culture & Ethics History of AI Groundwork for AI: 1900-1950In the early 1900s, there was a lot of media created that centered around the idea of artificial humans. So much so that scientists of all sorts started asking the question: is it possible to create an artificial brain? Some creators even made some versions of what we now call “robots” (and the word was coined in a Czech play in 1921) though most of them were simple. These were steam-powered, and some could make facial expressions and even walk. 1921: Czech playwright Karel Čapek released a science fiction play “Rossum’s Universal Robots” which introduced the idea of “artificial people” which he named robots. This was the first known use of the word. 1929: Japanese professor Makoto Nishimura built the first Japanese robot, named Gakutensoku. 1949: Computer scientist Edmund Callis Berkley published the book “Giant Brains, or Machines that Think” which compared the newer models of computers to human brains. Birth of AI: 1950-1956 This range of time was when the interest in AI really came to a head. Alan Turing published his work “Computer Machinery and Intelligence” which eventually became The Turing Test, which experts used to measure computer intelligence. The term “artificial intelligence” was coined and came into popular use. 1950: Alan Turing published “Computer Machinery and Intelligence” which proposed a test of machine intelligence called The Imitation Game. 1952: A computer scientist named Arthur Samuel developed a program to play checkers, which is the first to ever learn the game independently. 1955: John McCarthy held a workshop at Dartmouth on “artificial intelligence” which is the first use of the word, and how it came into popular usage. AI maturation: 1957-1979
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