Big data
Md. Rezaul Karim
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis.This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.
Kaushik Balakrishnan
Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.
Teradata Cookbook. Over 85 recipes to implement efficient data warehousing solutions
Abhinav Khandelwal, Rajsekhar Bhamidipati
Teradata is an enterprise software company that develops and sells its eponymous relational database management system (RDBMS), which is considered to be a leading data warehousing solutions and provides data management solutions for analytics. This book will help you get all the practical information you need for the creation and implementation of your data warehousing solution using Teradata.The book begins with recipes on quickly setting up a development environment so you can work with different types of data structuring and manipulation function. You will tackle all problems related to efficient querying, stored procedure searching, and navigation techniques. Additionally, you’ll master various administrative tasks such as user and security management, workload management, high availability, performance tuning, and monitoring.This book is designed to take you through the best practices of performing the real daily tasks of a Teradata DBA, and will help you tackle any problem you might encounter in the process.
Joshua Burkhow, Justin Bozonier, Stefano Paluello
Mercury Learning and Information, Andres Fortino
This book leverages advanced techniques and tools in data science to extend data analysis from numeric and categorical data to textual data. Designed for business analysts, it uses a case study approach to teach skills in extracting insights from text data, supporting business decision-making. Exercises primarily use Excel and R, covering techniques from basic text analytics to sophisticated methods like topic extraction and text similarity scoring.The course begins with framing analytical questions and exploring analytical tool sets. It progresses through preparing data files, performing word frequency and keyword analysis, and conducting sentiment analysis. Advanced topics include visualizing text data, coding, named entity recognition, and topic recognition in documents. The book also covers text similarity scoring and the analysis of large datasets by sampling.Throughout this journey, readers will apply the CRISP-DM data mining standard, using companion files with numerous datasets for practical exercises. By the end, participants will have a comprehensive understanding of text analytics, enabling them to derive meaningful insights from textual data to inform business strategies.
MrExcel's Holy Macro! Books, Kasper de Jonge
This book empowers readers to build professional dashboards and reports using Power BI. Starting with the basics of dashboards and reports, it delves into preparing data for visualizations, crafting detailed reports, and designing cohesive dashboards. The book provides clear methods for sharing work efficiently within organizations, ensuring that readers understand how to convert raw data into actionable insights.Practical examples and techniques throughout the book equip professionals with the skills they need to enhance their business intelligence capabilities. Readers will discover how to structure data for clarity, develop key reporting techniques, and integrate advanced features to maximize Power BI's potential.Whether you're a beginner or looking to refine your skills, this book offers a step-by-step approach to mastering Power BI's core capabilities, ensuring a seamless transition from data to decision-making.
Dr. Chun Schiros, Supreet Kaur, Rajdeep Arora,...
AI is only as valuable as the business outcomes it enables, and this hands-on guide shows you how to make that happen. Whether you’re a technology leader launching your first AI use case or scaling production systems, you need a clear path from innovation to impact. That means aligning your AI initiatives with enterprise strategy, operational readiness, and responsible practices, and The AI Optimization Playbook gives you the clarity, structure, and insight you need to succeed.Through actionable guidance and real-world examples, you’ll learn how to build high-impact AI strategies, evaluate projects based on ROI, secure executive sponsorship, and transition prototypes into production-grade systems. You’ll also explore MLOps and LLMOps practices that ensure scalability, reliability, and governance across the AI lifecycle.But deployment is just the beginning. This book goes further to address the crucial need for Responsible AI through frameworks, compliance strategies, and transparency techniques. Written by AI experts and industry leaders, this playbook combines technical fluency with strategic perspective to bridge the business–technology divide so you can confidently lead AI transformation across the enterprise.*Email sign-up and proof of purchase required
Alex Galea
From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security.Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples.Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data.By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects.