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Machine learning with TensorFlow For Dummies

Machine learning with TensorFlow For Dummies

Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. 

Business Analytics for Managers, 2ed: Taking Business Intelligence Beyond Reporting

Business Analytics for Managers, 2ed: Taking Business Intelligence Beyond Reporting

Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning.

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

The next generation of problems will not have deterministic solutions the solutions will be statistical that rely on mountains or mounds, of data. Bayesian methods offer a very flexible and extendible framework to solve these types of problems. For programming students with minimal background in mathematics, this example-heavy guide emphasizes the New technologies that have allowed the inference to be abstracted from complicated underlying mathematics. Using Bayesian Methods for Hackers, students can start leveraging powerful Bayesian tools right now gradually deepening their theoretical knowledge while already achieving powerful results in areas ranging from marketing to finance. Students will master Bayesian techniques that will play an increasingly crucial role in every data scientist's toolkit
Shows students how to solve statistically-based problems relying on mountains of data
Teaches through realistic (non-toy) examples built with the Python PyMC library, including start-to-finish application case studies
Gives an intuitive understanding of key concepts such as clustering, convergence, autocorrelation and thinning Chapter 1: the Philosophy of Bayesian Inference
Chapter 2: A Little More on PyMC
Chapter 3: Opening the Black Box of MCMC
Chapter 4: the Greatest Theorem Never Told
Chapter 5: the Greatest Theorem Never Told
Chapter 6: Getting Our Priorities Straight
Chapter 7: Bayesian A/B Testing.

Artificial Intelligence 3e: A Modern Approach

Artificial Intelligence 3e: A Modern Approach

This edition captures the changes that have taken place in the field of artificial intelligence (AI) since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. There has also been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning and computer vision.

ARTIFICIAL INTELLIGENCE Third Edition

ARTIFICIAL INTELLIGENCE Third Edition

This book presents both theoretical foundations of AI and an indication of the ways that current techniques can be used in application programs. With the revision, most of the content has been preserved as it is, and an effort has been put in on adding new topics that are in sync with the recent developments in this field.

Data Smart: Using Data Science to Transform Information into Insight (MISL-WILEY)

Data Smart: Using Data Science to Transform Information into Insight (MISL-WILEY)

The book provides nine tutorials on optimization, machine learning, data mining, and forecasting all within the confines of a spreadsheet. Each tutorial uses a real-world problem and the author guides the reader using query's the reader might ask as how to craft a solution using the correct data science technique. Hosting these nine spreadsheets for download will be necessary so that the reader can work the problems along with the book.

Machine Learning

Machine Learning

This book offers the readers The basics of machine learning in a very simple, user-friendly language. While browsing the table of Contents, you will realize that you are given an introduction to every concept that comes under the umbrella of machine learning. This book is aimed at students who are new to the topic of machine learning. It is meant for students studying machine learning in their undergraduate and postgraduate courses in information Technology. It is also aimed at computer engineering students. It will help familiarize students with the Terms and terminologies used in machine learning. We hope that this book serves as an entry point for students to pursue their future studies and careers in machine learning.

The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics -  both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”

Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: “The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.”

Karolis Urbonas, Head of Data Science at Amazon: “A great introduction to machine learning from a world-class practitioner.”

Sujeet Varakhedi, Head of Engineering at eBay: “Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.”

Everything you really need to know in Machine Learning in a hundred pages.

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