IBM Watson Solutions for Machine Learning: Achieving Successful Results Across Computer Vision, Natural Language Processing and AI Projects Using Watson Cognitive Tools (English Edition)

Description

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. 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 About the Authors Arindam Ganguly has been working in one of the top multinational companies in India for several years. He is a Machine Learning Engineer and has proven his knowledge in several domains. He has completed his Masters in Computer Applications and also teaches in several tech forums Cover Page Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Introduction to Machine Learning Structure Objectives Artificial intelligence and machine learning Types of machine learning Supervised machine learning Unsupervised machine learning Linear regression Python code for linear regression Support vector machine K-nearest neighbour classifier Conclusion 2. Deep Learning Structure Objectives A brief history of deep learning Biological neuron Artificial neuron Perceptron Sigmoid neuron Gradient descent and backpropagation Gradient descent Feed forward neural network Backpropagation TensorFlow and Keras Conclusion 3. Features and Metrics Structure Objectives Python refresher Data analysis with pandas Metrics Conclusion 4. Build Your Own Chatbot Structure Objectives What is Watson Assistant? Intents Entities Dialogs First ever project - the chatbot Create an IBM ID Create a resource The use case Create your intents Create your entities Create the dialogs Conclusion 5. First Complete Machine Learning Project Structure Objectives Downloading the data Setting up our environment Data analysis Data preparation Model training Storing and deploying the model in your Watson Machine Learning repository Conclusion 6. Perfecting Our Model Structure Objectives Decision tree Random forest Grid search cross validation Auto AI Prepare dataset Conclusion 7. Visual Recognition Structure Objectives Setting up the environment for Watson visual recognition Identify the visual recognition problem Create custom model Adding image data Training Conclusion 8. Watson Discovery Structure Objectives Natural Language Processing Scrape and find the right resources Analyse your documents Convert document data into raw formats Filter out junk data Convert the text to a number using pre-trained embedding Apply your model NLP with IBM Watson Discovery Setting up the environment Watson discovery news collection Querying Watson discovery collection Working with new collections Annotating Conclusion 9. Deployment and Others Structure Objectives Building a Python server Deploying your web application to IBM Cloud IBM Watson Tone Analyser IBM Watson Text to Speech Conclusion 10. Deploy the Food Ordering Bot Structure Objectives Link your dialog skill to your assistant Other ways to deploy a Watson Assistant Create a Python application to use the Watson Assistant API Conclusion Index