This helpful guide will walk you through the fundamentals of structured machine learning with its extensive notes, tables, and examples. Author Matt Harrison provides an insightful manual that you can use as a handy reference for your next machine learning project or as extra help during training. This book is great for programmers, data scientists, and AI engineers because it explains machine learning in general and shows you how to classify structured data. Among other things, you'll learn how to reduce dimensionality, predict a continuous value (regression), and cluster data.
This useful guide offers nearly 200 self-contained recipes to assist you in resolving machine learning issues you could run into throughout the course of your regular job. You will be able to address specific issues like importing data, handling text or numerical data, model selection, dimensionality reduction, and many other topics if you are familiar with Python and its libraries, like pandas and scikit-learn.
An extensive introduction to machine learning that unifies inference and probabilistic models. Automated data analysis techniques are necessary given the Web-enabled flood of electronic data we face today. These are provided by machine learning, which creates techniques that can automatically find patterns in data and utilize those patterns to forecast upcoming data. Based on a unified, probabilistic approach, this textbook provides a thorough and self-contained introduction to the area of machine learning.
Quantum Machine Learning fills the gap between theoretical advances in quantum computing and practical machine learning research. It aims to provide a synthesis that explains the most significant machine learning algorithms in a quantum environment while reducing the complexity of the disciplines involved.
The development of technology in our contemporary cultures depends critically on the ability to create molecules and materials with the appropriate qualities. This calls for the capacity to efficiently sample potential energy surfaces in order to get related macroscopic properties, as well as the ability to compute precisely microscopic features, such as energies, forces, and electrostatic multipoles of particular configurations.
Each type of machine learning is broken down in the book, along with how it functions and is applied in various industries. Different sorts of algorithms (supervised, unsupervised, and so on) used in machine learning training stages are also described. The reader will discover that any developer or IT specialist may extract information from their current data if they have the appropriate tools. The book describes the main categories of machine learning and offers coded answers for instances from the actual world. The two main pillars of machine learning, data preparation and data cleaning, are heavily stressed. Each chapter contains samples of the code in use as well as how it operates.
Large data sets are profitable ventures. However, merely possessing the data and the processing capacity to handle it is insufficient to provide significant outcomes. Technology and marketing executives who want to break through the noise and deliver tangible results that improve their bottom line will find Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners to be an all-inclusive reference. The book offers a detailed look at how big data analytics can be used to promote efficiency and good change by giving an interesting, comprehensive assessment of the state of big data analytics today and the expanding trend toward high performance computing architectures.
Alpaydin Ethem (Author)
An introduction to the main machine learning techniques is given in this textbook, which serves as a single source. It is meant for researchers and developers in the field, as well as advanced undergraduate and graduate students. Prior knowledge of statistics or artificial intelligence is not required. The World Wide Web provides access to many important algorithms, sample date sets, and project-oriented homework assignments covered in the book.
