No Code Ai Concepts And Applications In Machine Learning Visualization And Cloud Platforms
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No Code Ai Concepts And Applications In Machine Learning Visualization And Cloud Platforms
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Author : Minsoo Kang
language : en
Publisher:
Release Date : 2024-09-19
No Code Ai Concepts And Applications In Machine Learning Visualization And Cloud Platforms written by Minsoo Kang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-19 with Computers categories.
This book is a beginner-friendly guide to artificial intelligence (AI), ideal for those with no technical background. It introduces AI, machine learning, and deep learning basics, focusing on no-code methods for easy understanding. The book also covers data science, data mining, and big data processing, maintaining a no-code approach throughout. Practical applications are explored using no-code platforms like Microsoft Azure Machine Learning and AWS SageMaker. Readers are guided through step-by-step instructions and real-data examples to apply learning algorithms without coding. Additionally, it includes the integration of business intelligence tools like Power BI and AWS QuickSight into machine learning projects.This guide bridges the gap between AI theory and practice, making it a valuable resource for beginners in the field.
No Code Artificial Intelligence
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Author : Ambuj Agrawal
language : en
Publisher: BPB Publications
Release Date : 2023-03-07
No Code Artificial Intelligence written by Ambuj Agrawal and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-07 with Computers categories.
A practical guide that will help you build AI and ML solutions faster with fewer efforts and no programming knowledge KEY FEATURES ● Start your journey to become an AI expert today. ● Learn how to build AI solutions to solve complex problems in your organization. ● Get familiar with different No-code AI tools and platforms. DESCRIPTION “No-Code Artificial Intelligence” is a book that enables you to develop AI applications without any programming knowledge. Authored by the founder of AICromo (https://aicromo.com/), this book takes you through an array of examples that shows how to build AI solutions using No-code AI tools. The book starts by sharing insights on the evolution of No-code AI and the different types of No-code AI tools and platforms available in the market. The book then helps you start building applications of Machine Learning in Finance, Healthcare, Sales, and Cybersecurity. It will also teach you to create AI applications to perform sales forecasting, find fraudulent claims, and detect diseases in plants. Furthermore, the book will show how to build Machine Learning models for a variety of use cases in image recognition, video object recognition, and data prediction. After reading this book, you will be able to build AI applications with ease. WHAT YOU WILL LEARN ● Use different No-code AI tools such as AWS Sagemaker, DataRobot, and Google AutoML. ● Learn how to create a Machine Learning model to predict housing prices. ● Build Natural Language Processing (NLP) models for Healthcare information Identification. ● Learn how to build an AI model to create targeted customer offerings. ● Use traditional ways to perform AI implementation using programming languages and AI libraries. WHO THIS BOOK IS FOR This book is for anyone who wants to build an AI app without writing any code. It is also helpful for current and aspiring AI and Machine Learning professionals who are looking to build automated, intelligent, and smart AI-based solutions. TABLE OF CONTENTS 1. What is AI? 2. Getting Started with No-Code AI 3. Building AI Model to Predict Housing Prices 4. Classifying Different Images 5. Building AI Model to Perform Sales Forecasting 6. Building AI Model to Find Fraudulent Claims 7. Building AI Model to Detect Diseases in Plants 8. Building AI Model to Create Targeted Customer Offerings 9. Building AI Model for Healthcare Information Identification 10. Building AI Model for Video Action Recognition 11. Building AI Applications with Coded AI
Pragmatic Ai
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Author : Noah Gift
language : en
Publisher:
Release Date : 2018
Pragmatic Ai written by Noah Gift and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial intelligence categories.
Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—;even if you don't have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you'll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you'll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you're a business professional, decision-maker, student, or programmer, Gift's expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you'll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Deep Learning With Azure
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Author : Mathew Salvaris
language : en
Publisher: Apress
Release Date : 2018-08-24
Deep Learning With Azure written by Mathew Salvaris and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-24 with Computers categories.
Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
Low Code Ai
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Author : Gwendolyn Stripling
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-09-13
Low Code Ai written by Gwendolyn Stripling and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-13 with Computers categories.
Take a data-first and use-case–driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. You'll learn how to: Distinguish between structured and unstructured data and the challenges they present Visualize and analyze data Preprocess data for input into a machine learning model Differentiate between the regression and classification supervised learning models Compare different ML model types and architectures, from no code to low code to custom training Design, implement, and tune ML models Export data to a GitHub repository for data management and governance
Essentials Of Deep Learning And Ai
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Author : Shashidhar Soppin
language : en
Publisher: BPB Publications
Release Date : 2021-11-25
Essentials Of Deep Learning And Ai written by Shashidhar Soppin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-25 with Computers categories.
Drives next generation path with latest design techniques and methods in the fields of AI and Deep Learning KEY FEATURES ● Extensive examples of Machine Learning and Deep Learning principles. ● Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings. ● Numerous use cases with the code snippets and examples are presented. DESCRIPTION 'Essentials of Deep Learning and AI' curates the essential knowledge of working on deep neural network techniques and advanced machine learning concepts. This book is for those who want to know more about how deep neural networks work and advanced machine learning principles including real-world examples. This book includes implemented code snippets and step-by-step instructions for how to use them. You'll be amazed at how SciKit-Learn, Keras, and TensorFlow are used in AI applications to speed up the learning process and produce superior results. With the help of detailed examples and code templates, you'll be running your scripts in no time. You will practice constructing models and optimise performance while working in an AI environment. Readers will be able to start writing their programmes with confidence and ease. Experts and newcomers alike will have access to advanced methodologies. For easier reading, concept explanations are presented straightforwardly, with all relevant facts included. WHAT YOU WILL LEARN ● Learn feature engineering using a variety of autoencoders, CNNs, and LSTMs. ● Get to explore Time Series, Computer Vision and NLP models with insightful examples. ● Dive deeper into Activation and Loss functions with various scenarios. ● Get the experience of Deep Learning and AI across IoT, Telecom, and Health Care. ● Build a strong foundation around AI, ML and Deep Learning principles and key concepts. WHO THIS BOOK IS FOR This book targets Machine Learning Engineers, Data Scientists, Data Engineers, Business Intelligence Analysts, and Software Developers who wish to gain a firm grasp on the fundamentals of Deep Learning and Artificial Intelligence. Readers should have a working knowledge of computer programming concepts. TABLE OF CONTENTS 1. Introduction 2. Supervised Machine Learning 3. System Analysis with Machine Learning/Un-Supervised Learning 4. Feature Engineering 5. Classification, Clustering, Association Rules, and Regression 6. Time Series Analysis 7. Data Cleanup, Characteristics and Feature Selection 8. Ensemble Model Development 9. Design with Deep Learning 10. Design with Multi Layered Perceptron (MLP) 11. Long Short Term Memory Networks 12. Autoencoders 13. Applications of Machine Learning and Deep Learning 14. Emerging and Future Technologies.
Python Machine Learning From Scratch
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Author : Jonathan Adam
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-08-24
Python Machine Learning From Scratch written by Jonathan Adam and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-24 with categories.
***** BUY NOW (will soon return to 25.89 $)******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning.Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected] Sciences Company offers you a free eBooks at http://aisciences.net/free/
Ibm Watson Solutions For Machine Learning
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Author : Arindam Ganguly
language : en
Publisher: BPB Publications
Release Date : 2021-06-19
Ibm Watson Solutions For Machine Learning written by Arindam Ganguly and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-19 with Computers categories.
Utilize Python and IBM Watson to put real-life use cases into production. KEY FEATURES ● Use of popular Python packages for building Machine Learning solutions from scratch. ● Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. ● Expert-led best practices to put your Machine Learning solutions into the production environment. DESCRIPTION This book will take you through the journey of some amazing tools IBM Watson has to offer to leverage your machine learning concepts to solve some real-life use cases that are pertinent to the current industry. This book explores the various Machine Learning fundamental concepts and how to use the Python programming language to deal with real-world use cases. It explains how to take your code and deploy it into IBM Cloud leveraging IBM Watson Machine Learning. While doing so, the book also introduces you to several amazing IBM Watson tools such as Watson Assistant, Watson Discovery, and Watson Visual Recognition to ease out various machine learning tasks such as building a chatbot, creating a natural language processing pipeline, or an optical object detection application without a single line of code. It covers Watson Auto AI with which you can apply various machine learning algorithms and pick out the best for your dataset without a single line of code. Finally, you will be able to deploy all of these into IBM Cloud and configure your application to maintain the production-level runtime. After reading this book, you will find yourself confident to administer any machine learning use case and deploy it into production without any hassle. You will be able to take up a complete end-to-end machine learning project with complete responsibility and deliver the best standards the current industry has to offer. Towards the end of this book, you will be able to build an end-to-end production-level application and deploy it into Cloud. WHAT YOU WILL LEARN ● Review the basics of Machine Learning and learn implementation using Python. ● Learn deployment using IBM Watson Studio and Watson Machine Learning. ● Learn how to use Watson Auto AI to automate hyperparameter tuning. ● Learn Watson Assistant, Watson Visual Recognition, and Watson Discovery. ● Learn how to implement the various layers of an end-to-end AI application. ● Learn all the configurations needed for production deployment to Cloud. WHO THIS BOOK IS FOR This book is for all data professionals, ML enthusiasts, and software developers who are looking for real solutions to be developed. The reader is expected to have a prior knowledge of the web application architecture and basic Python fundamentals. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Deep Learning 3. Features and Metrics 4. Build Your Own Chatbot 5. First Complete Machine Learning Project 6. Perfecting Our Model 7. Visual Recognition 8. Watson Discovery 9. Deployment and Others 10. Deploying the Food Ordering Bot
Machine Learning Concepts Tools And Data Visualization
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Author : Minsoo Kang
language : en
Publisher: World Scientific
Release Date : 2021-03-16
Machine Learning Concepts Tools And Data Visualization written by Minsoo Kang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Computers categories.
This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book.
Hands On Explainable Ai Xai With Python
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
Hands On Explainable Ai Xai With Python written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Computers categories.
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications