Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data (Patel Ankur A.)(Paperback)

Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data (Patel Ankur A.)(Paperback)

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the

Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python (Tatsat Hariom)(Paperback)

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies

Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch (Ketkar Nikhil)(Paperback)

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how

Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit (Pattanayak Santanu)(Paperback)

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic

Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance (Mathur Puneet)(Paperback)

Part 1: HealthcareChapter 1. Overview of machine learning in healthcare.Chapter 2. Key technological advancements in healthcare.Chapter 3. How to implement machine learning in healthcare.Chapter 4. Case studies on how organizations are changing the game in the market.Chapter 5. Pitfalls to avoid

Learn Pyspark: Build Python-Based Machine Learning and Deep Learning Models (Singh Pramod)(Paperback)

Learn PySparkChapter 1: Introduction to PySparkChapter 2: Data ProcessingChapter 3: Spark Structured StreamingChapter 4: AirflowChapter 5: Machine Learning Library (MLlib)Chapter 6: Supervised Machine LearningChapter 7: Unsupervised Machine LearningChapter 8: Deep Learning Using PySpark

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning (Bengfort Benjamin)(Paperback)

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data

Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Lakshmanan Valliappa)(Paperback)

By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification,

Data Science on AWS: Implementing End-To-End, Continuous AI and Machine Learning Pipelines (Fregly Chris)(Paperback)

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills

Introduction to Machine Learning with Python: A Guide for Data Scientists (Mller Andreas C.)(Paperback)

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning

Practical Deep Learning: A Python-Based Introduction (Kneusel Ron)(Paperback)

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you've been curious about machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine

Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (Nlp) Applications (Albrecht Jens)(Paperback)

Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work

Building Machine Learning Powered Applications: Going from Idea to Product (Ameisen Emmanuel)(Paperback)

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including

Deep Learning from Scratch: Building with Python from First Principles (Weidman Seth)(Paperback)

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You'll start with

Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing (Beysolow II Taweh)(Paperback)

Chapter 1: What is Natural Language Processing? Chapter Goal: Establishing understanding of topic and give overview of textNo of pages: 10 pagesSub -Topics1. History of Natural Language Processing 2. Word Embeddings3. Neural Networks applied to Natural Language Processing 4. Python Packages Chapter

Machine Learning, Revised and Updated Edition (Alpaydin Ethem)(Paperback)

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we

Kubeflow for Machine Learning: From Lab to Production (Grant Trevor)(Paperback)

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving

Building Distributed Applications in Gin: A hands-on guide for Go developers to build and deploy distributed web apps with the Gin framework (Labouardy Mohamed)(Paperback)

An effective guide to learning how to build a large-scale distributed application using the wide range of functionalities in GinKey Features: Explore the commonly used functionalities of Gin to build web applicationsBecome well-versed with rendering HTML templates with the Gin engineSolve commonly

Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python (Orland Paul)(Paperback)

In Math for Programmers you'll explore important mathematical concepts through hands-on coding. Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these

The MVP Machine: How Baseball's New Nonconformists Are Using Data to Build Better Players (Lindbergh Ben)(Paperback)

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow (Koul Anirudh)(Paperback)

Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications

Inquiry-Based Learning Using Everyday Objects: Hands-On Instructional Strategies That Promote Active Learning in Grades 3-8 (Alvarado Amy Edmonds)(Paperback)

This exciting new book explores how students can use everyday objects to answer essential questions, meet curriculum standards, and grow in observation, inquisitiveness, and reflective

Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C# (Prez Castao Arnaldo)(Paperback)

Practical Artificial IntelligenceChapter 1: Logic & AIChapter 2: Automated Theorem Proving & First Order LogicChapter 3: AgentsChapter 4: Mars RoverChapter 5: Multi-Agent SystemsChapter 6: Communication in a Multi-Agent System using WCFChapter 7: Cleaning Agents: A multi-Agent System ProblemChapter

Learning RSLogix 5000 Programming: Building PLC solutions with Rockwell Automation and RSLogix 5000 (Scott Austin)(Paperback)

Become proficient in building PLC solutions in Integrated Architecture from the ground up using RSLogix 5000Key Features Introduction to the Logix platform and Rockwell Automation terminology, with resources available online in the literature library Build real-world Rockwell Automation solutions