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

3 Book Recommendations by Ines Montani

  • 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.