Applied Machine Learning With Scikit Learn
Download Applied Machine Learning With Scikit Learn PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Applied Machine Learning With Scikit Learn book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Applied Machine Learning with Scikit-learn
"Applied Machine Learning with Scikit-learn" "Applied Machine Learning with Scikit-learn" is a comprehensive and in-depth guide that empowers readers to build robust machine learning solutions using the popular Scikit-learn library. The book navigates through the complete lifecycle of machine learning projects, starting from the foundational architecture and integration of Scikit-learn within the broader PyData ecosystem, to advanced data preparation, feature engineering, and the design of custom components. Readers benefit from best practices in scalability, reproducibility, and extensibility, while gaining insights into contributing to and extending the library to suit cutting-edge applications. A core strength of this book is its rigorous treatment of both supervised and unsupervised learning techniques. It offers advanced coverage on classification and regression models—including linear methods, ensemble approaches, support vector machines, and probabilistic classifiers—while addressing practical challenges like imbalanced data, custom scoring, and evaluation strategies. The unsupervised learning chapters explore clustering, dimensionality reduction, density estimation, and feature discovery, complete with methodologies for model selection, validation, and interpretation. Specialized sections on experiment tracking, hyperparameter tuning, and prevention of data leakage ensure that readers can conduct reliable analyses in research or production settings. Recognizing the growing importance of model deployment, monitoring, and integration, the book dedicates ample attention to scaling workflows, building production-grade APIs, automating model retraining, and complying with security and privacy standards. Advanced topics guide practitioners through contemporary machine learning frontiers—such as AutoML, hybrid deep learning integration, time series analysis, weakly supervised learning, and graph-based models. By merging practical implementation advice with a deep understanding of the underlying principles, "Applied Machine Learning with Scikit-learn" serves as an invaluable reference for data scientists, engineers, and researchers striving to leverage the full potential of Scikit-learn in modern machine learning endeavors.
Machine Learning with PyTorch and Scikit-Learn
Author: Sebastian Raschka
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-02-25
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Applied Deep Learning with Python
A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.