Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The "secret sauce" of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.

TypeScript is a superset of the JavaScript programming language, giving developers a tool to help them write faster, cleaner JavaScript. With the help of its powerful static type system and other powerful tools and techniques it allows developers to write modern JavaScript applications.This book is a practical guide to learn the TypeScript programming language. It covers from the very basics to the more advanced concepts, while explaining many design patterns, techniques, frameworks, libraries and tools along the way. You will also learn a ton about modern web frameworks like Angular, Vue.js and React, and you will build cool web applications using those. This book also covers modern front-end development tooling such as Node.js, npm, yarn, Webpack, Parcel, Jest, and many others. Throughout the book, you will also discover and make use of the most recent additions of the language introduced by TypeScript 3 such as new types enforcing explicit checks, flexible and scalable ways of project structuring, and many more breaking changes.By the end of this book, you will be ready to use TypeScript in your own projects and will also have a concrete view of the current frontend software development landscape.

TypeScript is a superset of the JavaScript programming language, giving developers a tool to help them write faster, cleaner JavaScript. With the help of its powerful static type system and other powerful tools and techniques it allows developers to write modern JavaScript applications.This book is a practical guide to learn the TypeScript programming language. It covers from the very basics to the more advanced concepts, while explaining many design patterns, techniques, frameworks, libraries and tools along the way. You will also learn a ton about modern web frameworks like Angular, Vue.js and React, and you will build cool web applications using those. This book also covers modern front-end development tooling such as Node.js, npm, yarn, Webpack, Parcel, Jest, and many others. Throughout the book, you will also discover and make use of the most recent additions of the language introduced by TypeScript 3 such as new types enforcing explicit checks, flexible and scalable ways of project structuring, and many more breaking changes.By the end of this book, you will be ready to use TypeScript in your own projects and will also have a concrete view of the current frontend software development landscape.

Mining data requires a deep investment in people and time. How can you be sure you're building the right models? What tools help you connect with the customer's needs? With this hands-on book, you'll learn a flexible toolset and methodology for building effective analytics applications. Agile Data shows you how to create an environment for exploring data, using lightweight tool...(展开全部)

Mining data requires a deep investment in people and time. How can you be sure you're building the right models? What tools help you connect with the customer's needs? With this hands-on book, you'll learn a flexible toolset and methodology for building effective analytics applications. Agile Data shows you how to create an environment for exploring data, using lightweight tool...(展开全部)

Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools. Worki...(展开全部)

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.