Still unsure how Data Science and Cloud can be integrated? Or what’s the future of machine learning?
These books on various concepts of tech will answer these questions and more.
14. Integrated Analytics
This book is written by Courtney Webster. She is is a reformed chemist in the Washington, D.C. metro area and spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.
In this book, she talks about the platforms and principles for centralizing our Data. And she presents a roadmap to data centralization that will help our organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on our company’s ability to quickly act upon new findings.
This helps and explains to us on how to identify stakeholders to build a culture of trust and awareness among decision makers, data analysts, and quality management, how to create a data plan to define your needs, specify your metrics, identify data sources, and standardize metric definitions, how to centralize the data to evaluate each data source for existing common fields and, if we can, minor variances, and standardize data references, and find the right tools for the job to choose from legacy architecture tools, managed and cloud-only services, and data visualization or data exploration platforms. Click here to read, happy reading.
15. Embedding Analytics in Modern Applications
This book is written by Courtney Webster. She is a reformed chemist in the Washington, D.C. metro area. She spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.
In this, she shared how to provide distraction-free Insights to End Users. She reviews several approaches and methods for embedding analytics capabilities into our applications like should we implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do we need to build our own or buy a solution out of the box?
This book helps us to choose the right embedded analytics tool, and she examines seven challenges—from customization, usability, and capabilities to scalability, performance, and data structure support—and presents best practice solutions for each. Click here to read, happy reading.
16. Data Science in the Cloud
This book is written by Stephen F. Elston to share his view on Data Science in the Cloud with Microsoft Azure Machine Learning and Python. He is Managing Director of Quantia Analytics, LLC is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning.
He holds a PhD degree in Geophysics from Princeton University. In this book, he walks us through key steps in the data science process from problem definition, data understanding, and feature engineering, through the construction of a regression model and presentation of results. We’ll also learn how to extend Azure ML with Python.
Elston uses downloadable Python code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, we’ll be able to learn how to publish your trained models as web services in the Azure cloud. Click here to read, happy reading.
17. Evaluating Machine Learning Models
This book is written by Alice. She is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager on Amazon’s Ad Platform.
The author first introduces the machine-learning workflow and then dives into evaluation metrics and model selection. The latter half of the book focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners.
It includes how to learn the stages involved when developing a machine-learning model for use in a software application, how to understand the metrics used for supervised learning models, including classification, regression, and ranking, how to walk through evaluation mechanisms, such as hold/out validation, cross-validation, and bootstrapping, how to explore hyperparameter tuning in detail, and discover why it’s so difficult, how to learn the pitfalls of A/B testing and examine a promising alternatives like multi-armed bandits, and she shared few suggestions for further reading, as well as useful software packages. Click here to read, happy reading.
18. Artificial Intelligence Now
This is current Perspectives from O’Reilly Media. And they have shared about the AI landscape-the platforms, businesses, and business models shaping AI growth; plus a look at the emerging AI stack, Technology-AI’s technical underpinnings and deep learning capabilities, tools, and tutorials, Homebuilt autonomous systems- hobbyist applications that showcase AI tools, libraries, cloud processing, and mobile computing, Natural language-strategies for scoping and tackling NLP projects, Use cases- an analysis of two of the leading-edge use cases for artificial intelligence—chat bots and autonomous vehicles, Integrating human and machine intelligence-development of human-AI hybrid applications and workflows; using AI to map and access large-scale knowledge databases. Click here to read, happy reading.
19. Practical Artificial Intelligence in the Cloud
Mike Barlow is an award-winning journalist, author and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in numerous industries. He shared his view on exploring AI-as-a-Service for Business and Research.
Through interviews with consumers and executives of AIaaS vendors, the author examines the primary driver of this new approach including, AI is simply too big for any single device or system. But with AIaaS, developers can build applications that perform data collection and compression on devices, while advanced processes such as natural language processing and machine learning are performed in the cloud. Click here to read, happy reading.
20. What is Artificial Intelligence?
This book is written by Mike Loukides & Ben Lorica. Mike Loukides is an editor for O’Reilly Media, Inc. He is the author of System Performance Tuning and UNIX for FORTRAN Programmers. Mike’s interests are system administration, networking, programming languages, and computer architecture. Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O’Reilly Media, Inc.
He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. This book helps to have deeper look into the meaning of “general intelligence” when applied to AIs, Moving AIs from supervised learning to unsupervised learning, why AIs can easily solve problems that humans find challenging, but not problems that humans find easy, the differences between autonomous AIs and assistive AIs that augment our intelligence, factors that have made AI a hot topic in recent years, Today’s successful AI systems, such as machine learning and computer vision, and OpenAI and the push to make AI research open and visible to the public. Click here to read, happy reading.
21. The Future of Machine Intelligence
In this book, David shared the perspectives of future of Machine Intelligence from Leading Practitioners. David is an investor with Amplify Partners, an early-stage VC focused on the next generation of infrastructure IT, data and information security companies. He began his career in technology as the co-founder and CEO of Chartio.com, a pioneering provider of cloud-based data visualization and analytics.
He was subsequently part of the founding team of Patients Know Best, one of the world’s leading cloud-based Personal Health Record (PHR) companies. In this book, we are able to learn the following topic from various practitioners and theoreticians: high-dimensional problems and non-convex optimization, Natural Language Processing and deep learning, deep learning meets genomic medicine, the startling creativity of evolutionary algorithms, a synthesis of machine learning and control theory, the autonomous car as a driving partner, using topology to uncover the shape of our data, the promise of unsupervised learning and attention model, sequence-to-sequence machine learning, the evolution of machine learning and the role of Spark. Click here to read, happy reading.
22. What Are Conversational Bots?
This book is all an Introduction to and Overview of AI-Driven Chatbots. It is written by two authors, Mike Barlow is an award-winning journalist, author and communications strategy consultant. Jon Bruner is a data journalist who approaches questions that interest him by writing and coding. Before coming to O’Reilly, where he is editor-at-large, he was data editor at Forbes Magazine.
In this book, authors examine the promise of chatbots, as well as the challenges they faced. Driven by recent advances in artificial intelligence (AI), chatbots have a bright future in customer relations, healthcare, games and entertainment, and worker productivity (picture a bot as our personal assistant).
And moreover, Microsoft CEO Satya Nadella recently declared that “bots are the new apps.” And explore this book to explore today’s emerging chatbot landscape, including why chatbots now, messaging platforms/frameworks for bots, AI Platforms and Frameworks for bots, and Real-world examples. Click here to read, happy reading.
23. Architecting for Access
In this book, the author shared his view on how to simplifying the analytics on big data infrastructure. Rich Morrow is a 20 year veteran of IT and an expert big data technologies like Hadoop. He has been teaching Cloudera (Hadoop) and AWS for nearly 3 years, retains all certifications for both and uses these technologies in his day to day consulting practice.
He is a prolific writer on Cloud, Big Data, DevOps/Agile, Mobile, and IoT topics, having published many works for companies like GigaOM and Global Knowledge. He explained how the rapid changes to both backend storage and frontend analytics over the past decade, and provides a pragmatic list of requirements for an analytics stack that will centralize access to all of these data systems. It examines current analytics platforms, including looker—a new breed of analytics and visualization tools built specifically to handle our fragmented data space. Click here to read, happy reading.
24. Migrating Big Data Analytics to the Cloud
This book is written by Mike Barlow. He is an award-winning journalist, author and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in numerous industries. He shared his view on just how strong is the movement of big data analytics to the cloud.
And he shares that the desire among corporations to adopt big data-as-a-service is gaining momentum-and that many organizations with big data cloud experience are likely to expand their use. With King’s recent survey of IT and data professionals in finance, healthcare, technology, and telecom illustrate the ways many of them plan to use big data in the cloud with predictive analytics leading the charge-and explains why others are still reluctant to join the migration.
There are also some surprises, such as the continued popularity of relational databases and the lack of interest in social network analysis. If we are looking into big data cloud services, then this small book well worthwhile. Click here to read, happy reading.
25. Azure for Developers
This book is written by John Adams. He is a senior cloud solutions architect building in-depth custom training material around Microsoft Azure. He delivers on-site training courses, recorded online training videos, and builds hands-on labs and other instructional materials. He currently works for Opsgility.
In this book, he shared about what programmers need to know about Microsoft’s Cloud Platform. And he explains the Microsoft’s Azure platform which has a vast array of features like cloud hosting, web hosting, data analytics, data storage, machine learning, and more—all integrated with Visual Studio, the tool that .NET developers already know. Click here to read, happy reading.
To read the first part, click here.