Pattern Recognition & Machine Learning Using Python to understanding the fundamentals of pattern recognition and machine learning, with a hands-on approach using Python. This bridges theoretical concepts with practical applications, covering algorithms, data preprocessing, and model evaluation. It includes topics such as supervised and unsupervised learning, feature selection, and deep learning techniques. Ideal for students, researchers, and professionals, the emphasizes real-world examples and Python implementations to enhance learning and skill development in data-driven problem-solving.

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Understand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.

This book is a comprehensive guide to understanding and implementing cutting-edge machine learning and deep learning techniques using Python programming language. Written with both beginners and experienced developers in mind, this book provides a thorough overview of the foundations of machine learning and deep learning, including mathematical fundamentals, optimization algorithms, and neural networks. Starting with the basics of Python programming, this book gradually builds up to more advanced topics, such as artificial neural networks, convolutional neural networks, and generative adversarial networks. Each chapter is filled with clear explanations, practical examples, and step-by-step tutorials that allow readers to gain a deep understanding of the underlying principles of machine learning and deep learning. Throughout the book, readers will also learn how to use popular Python libraries and packages, including numpy, pandas, scikit-learn, TensorFlow, and Keras, to build and train powerful machine learning and deep learning models for a variety of real-world applications, such as regression and classification, K-means, support vector machines, and recommender systems. Whether you are a seasoned data scientist or a beginner looking to enter the world of machine learning, this book is the ultimate resource for mastering these cutting-edge technologies and taking your skills to the next level. High-school level of mathematical knowledge and all levels (including entry-level) of programming skills are good to start, all Python codes are available at Github.com. Table Of Contents 1 Introduction 1.1 Artificial Intelligence, Machine Learning and Deep Learning 1.2 Whom This Book Is For 1.3 How This Book Is Organized 2 Environments 2.1 Source Codes for This Book 2.2 Cloud Environments 2.3 Docker Hosted on Local Machine 2.4 Install on Local Machines 2.5 Install Required Packages 3 Math Fundamentals 3.1 Linear Algebra 3.2 Calculus 3.3 Advanced Functions 4 Machine Learning 4.1 Linear Regression 4.2 Logistic Regression 4.3 Multinomial Logistic Regression 4.4 K-Means Clustering 4.5 Principal Component Analysis (PCA) 4.6 Support Vector Machine (SVM) 4.7 K-Nearest Neighbors 4.8 Anomaly Detection 4.9 Artificial Neural Network (ANN) 4.10 Convolutional Neural Network (CNN) 4.11 Recommendation System 4.12 Generative Adversarial Network References About the Author

This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.

Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.

The book comprehensively covers the most important applications of the internet of things (IoT) using Python programming on Raspberry pi, Micropython Py Board, and NVIDIA Jetson Board. The authors have used an immersive ‘hands-on’ approach to help readers gain expertise in developing working code for real-world IoT applications. The book focuses on industry-standard embedded platforms for IoT applications. It also gives a glimpse of python programming and setup configuration of these embedded platforms. The later chapter highlights basic interface applications with Raspberry Pi. Exclusive advanced IoT applications on the Micropython Pyboard are also covered. The last two chapters deal with the NVIDIA Jetson Nano board programming for machine learning applications with FoG/cloud computing. The various IoT applications with different embedded platforms in this volume are best-suited for undergraduate/postgraduate students and researchers who want to get exposed to python programming for IoT applications. This book will enable readers to design their own embedded IoT products.

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. - Helps readers understand how open-source Python can be utilized in practical oil and gas challenges - Covers the most commonly used algorithms for both supervised and unsupervised learning - Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques

Easily Boost Your Skills In Python Programming & Become A Master In Deep Learning & Data Analysis! 💻 Python is an interpreted, high-level, general-purpose programming language that emphasizes code readability with its notable use of significant whitespace. What makes Python so popular in the IT industry is that it uses an object-oriented approach, which enables programmers to write clear, logical code for all types of projects, whether big or small. Hone your Python Programming skills and gain a sharp edge over other programmers the EASIEST way possible... with this practical beginner’s guide! In his 3-in-1 Python crash course for beginners, Anthony Adams gives novices like you simple, yet efficient tips and tricks to become a MASTER in Python coding for artificial intelligence, neural networks, machine learning, and data science/analysis! Here’s what you’ll get: ✅ Highly innovative ways to boost your understanding of Python programming, data analysis, and machine learning ✅ Quickly and effectively stop fraud with machine learning ✅ Practical and efficient exercises that make understanding Python quick & easy And so much more! As a beginner, you might feel a bit intimidated by the complexities of coding. Add the fact that most Python Programming crash course guides make learning harder than it has to be! ✓ With the help of this 3-in-1 guide, you will be given carefully sequenced Python Programming lessons that’ll maximize your understanding, and equip you with all the skills for real-life application! ★ Thrive in the IT industry with this comprehensive Python Programming crash course! ★ Scroll up, Click on “Buy Now”, and Start Learning Today!

Unlock the potential of data with "Streamlining ETL: A Practical Guide to Building Pipelines with Python and SQL," the definitive resource for creating high-performance ETL pipelines. This essential guide is meticulously designed for data professionals seeking to harness the data-intensive capabilities of Python and SQL. From establishing a development environment and extracting raw data to optimizing and securing data processes, this book offers comprehensive coverage of every aspect of ETL pipeline development. Whether you're a data engineer, IT professional, or a scholar in data science, this book provides step-by-step instructions, practical examples, and expert insights necessary for mastering the creation and management of robust ETL pipelines. By the end of this guide, you will possess the skills to transform disparate data into meaningful insights, ensuring your data processes are efficient, scalable, and secure. Dive into advanced topics with ease and explore best practices that will make your data workflows more productive and error-resistant. With this book, elevate your organization's data strategy and foster a data-driven culture that thrives on precision and performance. Embrace the journey to becoming an adept data professional with a solid foundation in ETL processes, equipped to handle the challenges of today's data demands.