This book is for machine learning professional & aspiring data scientist who wanted to be established themselves as a machine learning engineer or data science professional. Machine Learning Algorithms & Concepts gives complete idea to begin the phase of machine learning professional. This can be referred as a great starting point to switch the career path from existing profession to a machine learning professional. The book covers all major algorithms, its concept, usage, and other miscellaneous concepts based on situation which helps to its reader to decide in which situation what to be used. This book serves as guide to prepare for interviews, exams, campus work as well as for industry professional. It also covers basic programming which gives fair idea to its reader to learn how to code for machine learning problem statement even if he is a beginner in coding.

Dr.R.Gowri, Associate Professor, Department of Mathematics, Government College for Women (Autonomous), Kumbakonam, Tamil Nadu, India. Mrs.R.A.Latha Devi, Assistant Professor, Department of Mathematics, Sri Meenakshi Government Arts College for Women, Madurai, Tamil Nadu, India Dr.T.Dheepak, Assistant Professor, Department of Computer Science, Centre for Distance and Online Education, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India. Dr.P.Kavitha, Assistant Professor, Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts and Science for Women Autonomous, Perambalur, Tamil Nadu, India. Dr.T.Suresh, Assistant Professor, Department of Artificial Intelligence & Machine Learning, K.Ramakrishnan College of Engineering, Tiruchirappalli, Tamil Nadu, India.

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: • Monte Carlo Stock Price Simulation • Image Denoising using Mean-Field Variational Inference • EM algorithm for Hidden Markov Models • Imbalanced Learning, Active Learning and Ensemble Learning • Bayesian Optimization for Hyperparameter Tuning • Dirichlet Process K-Means for Clustering Applications • Stock Clusters based on Inverse Covariance Estimation • Energy Minimization using Simulated Annealing • Image Search based on ResNet Convolutional Neural Network • Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's inside • Monte Carlo stock price simulation • EM algorithm for hidden Markov models • Imbalanced learning, active learning, and ensemble learning • Bayesian optimization for hyperparameter tuning • Anomaly detection in time-series About the reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Table of Contents PART 1 1 Machine learning algorithms 2 Markov chain Monte Carlo 3 Variational inference 4 Software implementation PART 2 5 Classification algorithms 6 Regression algorithms 7 Selected supervised learning algorithms PART 3 8 Fundamental unsupervised learning algorithms 9 Selected unsupervised learning algorithms PART 4 10 Fundamental deep learning algorithms 11 Advanced deep learning algorithms

The book "Machine Learning Concepts for Beginners- Theory and Applications" provides the in-depth knowledge in the field of Machine Learning to graduate, post graduate and research scholars. Basically, machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Dr.Padmaja Pulicherla, Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. Dr.Kasarla Satish Reddy, Professor, Department of Electronics and Communication Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. D.Satyanarayana, Assistant Professor, Department of Computer Science and Engineering(DS), Santhiram Engineering College(Autonomous), Nandyal, Andhra Pradesh, India. Dr.R.Sudheer Babu, Associate Professor, Department of Electronics and Communication Engineering, G.Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, India. Dr.Ravi Babu Devareddi, Assistant Professor, Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India.

Introduction to the mathematical foundation for understanding and analyzing machine learning algorithms for AI students and researchers.

Key Features: Clear Explanations of Machine Learning Algorithms: The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly. Hands-On Approach: Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects. Comprehensive Coverage: From linear regression and support vector machines to decision trees and neural networks, the book covers a wide array of algorithms, giving you a solid foundation to explore diverse problem domains. Performance Evaluation Methods: Learn how to evaluate the effectiveness of your models, identify areas for improvement, and optimize their performance using industry-standard evaluation techniques. Data Preprocessing Techniques: Discover the critical elements of data preprocessing that lay the groundwork for building robust and accurate machine learning models. Time Series Forecasting: Explore advanced algorithms specifically designed for time series data, a critical component of numerous real-world applications. Appendix for Easy Reference: Access all parameters of commonly used machine learning algorithms in a handy appendix, facilitating efficient model tuning.

Algorithms and Theory of Computation Handbook, Second Edition in a two volume set, provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. New to the Second Edition: Along with updating and revising many of the existing chapters, this second edition contains more than 20 new chapters. This edition now covers external memory, parameterized, self-stabilizing, and pricing algorithms as well as the theories of algorithmic coding, privacy and anonymity, databases, computational games, and communication networks. It also discusses computational topology, computational number theory, natural language processing, and grid computing and explores applications in intensity-modulated radiation therapy, voting, DNA research, systems biology, and financial derivatives. This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics