Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Anand Deshpande, Manish Kumar

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.

Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding

Luis Sobrecueva

AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you.This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions.By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.

Azure Data Factory Cookbook. Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks - Second Edition

Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton

This new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each.You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.

Badanie danych. Raport z pierwszej linii działań

Rachel Schutt, Cathy O'Neil

Unikalne wprowadzenie do nauki o danych! W dzisiejszych czasach najcenniejszym dobrem jest informacja. Ogromne ilości danych są przechowywane w przepastnych bazach danych, a kluczem do sukcesu jest ich umiejętna analiza i wyciąganie wniosków. To dynamicznie rozwijająca się dziedzina wiedzy, w której do tej pory brakowało solidnych podręczników, pozwalających na dogłębne poznanie tego obszaru. Na szczęście to się zmieniło! To unikalna książka, w której badacze z największych firm branży IT dzielą się skutecznymi technikami analizy danych. Z kolejnych rozdziałów dowiesz się, czym jest nauka o danych, model danych oraz test A/B. Ponadto zdobędziesz wiedzę na temat wnioskowania statystycznego, algorytmów, języka R oraz wizualizacji danych. Sięgnij po tę książkę, jeżeli chcesz się dowiedzieć, jak wykrywać oszustwa, korzystać z MapReduce oraz badać przyczynowość. To obowiązkowa pozycja na półce czytelników zainteresowanych badaniem danych. Wśród tematów poruszonych w książce odnajdziesz: Wnioskowanie statystyczne, eksploracyjną analizę danych i proces (metodologię) nauki o danych Algorytmy Filtry spamu, naiwny algorytm Bayesa i wstępną obróbkę danych Regresję logistyczną Modelowanie finansowe Mechanizmy rekomendacji i przyczynowość Wizualizowanie danych Sieci społecznościowe i dziennikarstwo danych Inżynierię danych, systemy MapReduce, Pregel i Hadoop Wyciągnij wartościowe wnioski z posiadanych informacji!

Bayesian Analysis with Python. Click here to enter text

Osvaldo Martin

The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.

Bayesian Analysis with Python. Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ - Second Edition

Osvaldo Martin

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Become a Python Data Analyst. Perform exploratory data analysis and gain insight into scientific computing using Python

Alvaro Fuentes

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.

Becoming a Data Analyst. A Beginner's Guide to Kickstarting Your Data Analysis Career

Remsey Mailjard, Maaike van Putten

This guide is designed to take you from novice to confident data analyst. Starting with the fundamentals of data analytics, you will explore what data analysis entails and why it's crucial in today's data-driven industries. You'll develop a data analyst mindset, honing your problem-solving and critical-thinking skills through practical exercises. You'll be introduced to different types of data, data sources and key concepts like KPIs and data warehouses. Hands-on chapters will guide you through Excel for basic data analysis, teaching you vital functions, pivot tables, and visualization techniques. You'll dive into SQL to query and manipulate data as well as data cleaning and exploration to prepare datasets for meaningful analysis. More advanced chapters will introduce you to Power BI, so you can build interactive dashboards and use DAX for advanced calculations. You'll work on major projects that will form a professional portfolio showcasing your skills in sales analysis, HR analytics, and customer insights. Finally, the book will teach you the art of communicating your findings through data storytelling to different audiences. You'll also find guidance on continuing education and career growth, ensuring you're well-prepared to launch a successful career in data analytics.