Ines Montani

Ines Montani

β€Ž πŸ–€ Digital native & developer 🧠 AI, Machine Learning & NLP πŸ’₯ Founder @explosion_ai 🐍 Fellow @ThePSF πŸ‘©β€πŸ’» Developing @spacy_io and https://t.co/ny2jXQHun1 β€β€Ž

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4 Book Recommendations by Ines Montani

  • Mastering spaCy

    Duygu Altinok

    Build end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with ease Key Features: Gain an overview of what spaCy offers for natural language processing Learn details of spaCy's features and how to use them effectively Work through practical recipes using spaCy Book Description: spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications. You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results. By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps. What You Will Learn: Install spaCy, get started easily, and write your first Python script Understand core linguistic operations of spaCy Discover how to combine rule-based components with spaCy statistical models Become well-versed with named entity and keyword extraction Build your own ML pipelines using spaCy Apply all the knowledge you've gained to design a chatbot using spaCy Who this book is for: This book is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on with Python and spaCy and software developers who want to quickly prototype applications with spaCy will also find this book helpful. Beginner-level knowledge of the Python programming language is required to get the most out of this book. A beginner-level understanding of linguistics such as parsing, POS tags, and semantic similarity will also be useful.

    Look what I got in the mail today πŸ’– Thanks Duygu Altinok & @PacktPub! https://t.co/RKUL8T0vak https://t.co/sDnhvWCxX9

  • To really learn data science, you should not only master the tools-data science libraries, frameworks, modules, and toolkits-but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability-and how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, NaΓ―ve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

    @mariokostelac To take this full circle, I love the concept of "Data Science from Scratch" by @joelgrus: https://t.co/HzgPmktCKw

  • "For intermediate Python programmers"--Back cover.

    Since people were asking, the book shown here is "Classic Computer Science Problems in Python" by @davekopec. Also see Amazon for a less trippy preview reading experience πŸ˜‡ https://t.co/nPWvi9pZfa

  • Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    @Thom_Wolf @tymwol Haha πŸ˜‚ (Also, how is "NLP with BERT" even a reasonable book concept?) I've also received several of these emails... my favourite was "Machine Learning with R" because "according to my online profiles" I'd be "a good fit". I have never really done anything with R, like... ever.