Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models

Soledad Galli

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.

Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition

Soledad Galli

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.

Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python

Jason Strimpel

Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.

Python For Engineering and Scientific Computing. Practical Applications with NumPy, SciPy, Matplotlib, and More

Rheinwerk Publishing, Inc, Veit Steinkamp

This book provides a thorough introduction to Python programming designed for engineers and scientists. It begins with foundational topics like development environments and program structures, then introduces key Python libraries such as NumPy, SymPy, SciPy, Matplotlib, and VPython. Clear explanations and practical exercises help readers write efficient, well-structured code while progressing through increasingly complex projects.The content covers core programming paradigms including functions, branching, and object-oriented design, followed by numerical analysis with NumPy and symbolic math with SymPy. Detailed chapters on data visualization with Matplotlib and 3D animations with VPython enhance comprehension. Additional focus on statistical computations, Boolean algebra, and interactive GUI programming with Tkinter prepares readers for real-world applications. Each chapter ends with project tasks reinforcing hands-on learning.Throughout the book, readers build a strong skill set combining programming expertise and scientific problem-solving. By the end, they will confidently use Python’s libraries to solve diverse engineering and scientific challenges. This practical, project-based approach ensures knowledge is both solid and immediately useful in research and professional work.

Python for Finance. Apply powerful finance models and quantitative analysis with Python - Second Edition

Yuxing Yan

This book uses Python as its computational tool. Since Python is free, any school ororganization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance.The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory,options and futures.This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk,running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.

Python for TensorFlow Pocket Primer. A Quick Guide to Python Libraries for TensorFlow Developers

Mercury Learning and Information, Oswald Campesato

As part of the best-selling *Pocket Primer* series, this book prepares programmers for machine learning and deep learning with TensorFlow. It begins with a quick introduction to Python, followed by chapters on NumPy, Pandas, Matplotlib, and scikit-learn. The final chapters provide TensorFlow 1.x code samples, including detailed examples for TensorFlow Dataset, crucial for TensorFlow 2.The journey starts with Python basics and progresses through essential data manipulation and visualization libraries. You'll explore machine learning fundamentals with scikit-learn before diving into TensorFlow, learning to construct data pipelines with TensorFlow Dataset APIs like map(), filter(), and batch().Understanding these concepts is vital for modern AI applications. This book transitions readers from basic programming to advanced machine learning and deep learning techniques, blending theory with practical skills. Companion files with source code enhance learning, making this an essential resource for mastering Python, machine learning, and TensorFlow.

Python Fundamentals. A practical guide for learning Python, complete with real-world projects for you to explore

Ryan Marvin, Mark Nganga, Amos Omondi

After a brief history of Python and key differences between Python 2 and Python 3, you'll understand how Python has been used in applications such as YouTube and Google App Engine. As you work with the language, you'll learn about control statements, delve into controlling program flow and gradually work on more structured programs via functions.As you settle into the Python ecosystem, you'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.By the end of this book, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.

Python i AI dla e-commerce

Sebastian Kondracki

Sztuczna inteligencja według Pythona. Sięgnij po potężne wsparcie dla swojego e-sklepu E-commerce wspierany przez potężną moc sztucznej inteligencji ― to dla wielu właścicieli rodzimych firm internetowych wciąż brzmi jak odległa przyszłość. Może gdzieś tam, w Kalifornii, może u technologicznych gigantów, może Apple, Amazon, a bliżej nas, powiedzmy, Allegro korzysta lub będzie korzystać z chatbotów czy data-driven marketingu. Ale nasza firma do tej pory świetnie sobie radziła, to i dalej będzie sobie radzić z prostym mechanizmem sklepu online i kilkoma osobami obsługi. Marzenie ściętej głowy. Do 2025 roku w Polsce brakować będzie 200 tysięcy specjalistów w dziedzinie AI. I to nie w wielkich korporacjach. Głód programistów potrafiących kodować algorytmy sztucznej inteligencji odczują przede wszystkim firmy mniejsze i średnie. Jeśli jesteś właścicielem biznesu bazującego na sprzedaży w sieci, jeśli jesteś początkującym programistą albo działasz już jako programista e-commerce, ale chcesz się w tym kierunku rozwijać ― ta "książka kucharska" jest dla Ciebie. Dlaczego "książka kucharska"? Ponieważ podręcznik zawiera gotowe przepisy na algorytmy optymalizacyjne, systemy rekomendacyjne, przetwarzanie ogromnych ilości danych z ruchu odnotowanego w sklepie i zamianę ich w wiedzę o kliencie. Wszystko to już dziś wdrożysz w dowolnym e-sklepie stosunkowo małym kosztem. Zarówno przy użyciu gotowych programów napisanych w Pythonie przez ogromną społeczność miłośników AI i Pythona, jak i sprytnych produktów w modelu SaaS (ang. software as a service), sprzedawanych przez rzeszę polskich i zagranicznych startupów.

Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition

Alexander Combs, Michael Roman

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

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.

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!