Big data

Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition

Yuxi (Hayden) Liu

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Python Machine Learning By Example. Implement machine learning algorithms and techniques to build intelligent systems - Second Edition

Yuxi (Hayden) Liu

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

Python Machine Learning By Example. The easiest way to get into machine learning

Yuxi (Hayden) Liu

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.

Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition

Yuxi (Hayden) Liu

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts.Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.*Email sign-up and proof of purchase required

Python Machine Learning Cookbook. 100 recipes that teach you how to perform various machine learning tasks in the real world

Prateek Joshi

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition

Giuseppe Ciaburro, Prateek Joshi

This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III

Sebastian Raschka, Vahid Mirjalili

Uczenie maszynowe jest jedną z najbardziej fascynujących technologii naszych czasów - rozwojem jego najróżniejszych zastosowań zajmują się tacy giganci jak Google, Facebook, Apple, Amazon czy IBM. Uczenie maszynowe otwiera zupełnie nowe możliwości i powoli staje się nieodzowne: wystarczy wymienić asystenty głosowe w smartfonach, chatboty ułatwiające klientom wybór produktu, a także sieci ułatwiające podejmowanie decyzji o inwestycjach giełdowych, filtrujące niechciane wiadomości e-mail czy wspomagające diagnostykę medyczną. Oto obszerny przewodnik po uczeniu maszynowym i uczeniu głębokim w Pythonie. Zawiera dokładne omówienie najważniejszych technik uczenia maszynowego oraz staranne wyjaśnienie zasad rządzących tą technologią. Poszczególne zagadnienia zilustrowano mnóstwem wyjaśnień, wizualizacji i przykładów, co znakomicie ułatwia zrozumienie materiału i sprawne rozpoczęcie samodzielnego budowania aplikacji i modeli, takich jak te służące do klasyfikacji obrazów, odkrywania ukrytych wzorców czy wydobywania dodatkowych informacji z danych. Wydanie trzecie zostało zaktualizowane - znalazł się w nim opis biblioteki TensorFlow 2 i najnowszych dodatków do biblioteki scikit-learn. Dodano również wprowadzenie do dwóch nowatorskich technik: uczenia przez wzmacnianie i budowy generatywnych sieci przeciwstawnych (GAN). W książce między innymi: platformy, modele i techniki uczenia maszynowego wykorzystywanie biblioteki scikit-learn i TensorFlow sieci neuronowe, sieci GAN i inne przygotowywanie danych dla modeli uczenia maszynowego ocena i strojenie modeli analizy: regresyjna, skupień i sentymentów Uczenie głębokie z Pythonem: zrozum i zastosuj!

Python Machine Learning. Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

Sebastian Raschka

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.