Machine Learning

All Indian Reprints of O'Reilly are printed in Grayscale Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.

All Indian Reprints of O'Reilly are printed in Grayscale Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.

This small handy booklet has two- fold purpose : a. Mitigate the fear of statistics from the mind of beginners in the field of data analytics and research scholars of PhD who do not have background of statistics. It serves as comprehensive class note on statistics for them. b. Build the confidence of data scientists who extensively use the software packages for analysis of data as black boxes but do not have insight of how data crunching is done by the software, how type of statistical tests are selected , level of significance is fixed , how hypothesis is framed ,how statistical test are carried out in step-by-step manner and most importantly how interpretation of test outcomes are made. The basic philosophy of authors behind the book has been “ How to get more from less you read and more you read much more you get. About the Author Professor N. C. Das has been former Professor-cum-Chief Scientist at the Department of Statistics and Computer Science, Birsa Agricultural University, Ranchi, India. He has over six decades of teaching- and research-experience in the field of Statistical Inference, Design of Experiments, Operations Research and Computer Science. Data-based modelling for prediction has been integral part of above disciplines. These are now essential components of Data Science. It is natural therefore that he is drawn to simple model fitting to data for making prediction and its goodness of fit. To the same an acronym has been given SLIM & GUD-FIT for brevity, for other book has authored. He intensively devoted his time in advising and guiding a wide-spectrum of students, doctoral- and post doctoral –scholars and research specialists from various institutions, corporate organizations, core-sector industrial outfits and various consulting bodies on statistical aspects of research problems. He, as a Research Fellow of the International Development Agency at I.I.T. Kharagpur, had developed software BIVNOR which was required to be applied successfully for solving long aspired and much awaited problem of “Bivariate Joint Chance-Constrained Programming”. Later it was found to be of much wider use which culminated in publication of his monograph entitled “Decision Processes by Using Bivariate Normal Quantile Pairs” by Springer (2015). The said text also offers high probability joint confidence intervals to the much aspired measure (MAM) of Higgs Boson's particle, popularly called God particle, in case BEC (the magnitude of Bose-Einstein Correlation) is made available. According to World Cat the book by now has reached more than 283, amongst its more than 580 member libraries around the Globe, which according to them is considered fairly large acquisitions for such class of book-titles.  He remained Academic Secretary-cum-Editor of the Bihar Journal of Mathematics during 1994-1998 and is currently, as well as, the Founder President , Jharkhand Society of Mathematical Sciences: Ranchi. Dr. Mukesh Ranjan Das is Executive Director in one of the leading fortune 500 companies. He has acquired PhD in Management from IIT, Dhanbad in the area of Competency modelling. He graduated in Mechanical Engineering from B.I.T Mesra, Ranchi and later pursued PGCHRM from XLRI, Jamshedpur. He has published three research papers in Journals of repute and is serving as editorial board member of New York based journal "Humanities and Social Science'. He also serves in editorial board of "HR Vista" a quarterly digital journal of Oil Industry of India. He is also proud and blessed son of first author Professor N.C.Das.

Machine Learning based Prediction Techniques is a book for those who wanted to learn basics of Machine Learning and for those interested in undertaking projects, research, etc. The contents of book starts from the basics and extends to advanced content in a systematic manner. At the end of the book, learners will be able to develop their own predictive machine leaning based automated system.

All Indian Reprints of O'Reilly are printed in Grayscale This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. 

All Indian Reprints of O'Reilly are printed in Grayscale Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.

All Indian Reprints of O'Reilly are printed in Grayscale 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. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.  Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. 

All Indian Reprints of O'Reilly are printed in Grayscale Take advantage of the power of cloud and the latest AI techniques. Whether you're an experienced developer wanting to improve your app with AI-powered features or you want to make a business process smarter by getting AI to do some of the work, this book's got you covered. Authors Anand Raman, Chris Hoder, Simon Bisson, and Mary Branscombe show you how to build practical intelligent applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

All Indian Reprints of O'Reilly are printed in Grayscale 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 experienced practitioners and novices alike will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

All Indian Reprints of O'Reilly are printed in Grayscale Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world.

Free Shipping not applicable on this title. Checkout amount of ₹1,238 includes Shipping cost. Description: Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes...

All Indian Reprints of O'Reilly are printed in Grayscale With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention