assistant: Puede leer algunos de estos libros:
    Machine Learning: A Guide to Current Research por Tom M. Mitchell
    Click: What Millions of People Are Doing Online and Why it Matters por Bill Tancer
    Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking por Foster Provost
    Deep Learning por Ian Goodfellow
    Spidering Hacks por Kevin Hemenway
    Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems por Aurélien Géron
    Data Analysis with Open Source Tools por Philipp K. Janert
    R for Data Science: Import, Tidy, Transform, Visualize, and Model Data por Hadley Wickham
    Doing Data Science: Straight Talk from the Frontline por Cathy O'Neil
    Genetic Algorithms in Search, Optimization, and Machine Learning por David Edward Goldberg
    Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites por Matthew A. Russell
    Data Smart: Using Data Science to Transform Information into Insight por John W. Foreman
    Generative Programming: Methods, Tools, and Applications por Krysztof Czarnecki
    Python Data Science Handbook: Essential Tools for Working with Data por Jake VanderPlas
    An Introduction to Support Vector Machines and Other Kernel-based Learning Methods por Nello Cristianini
    Learning From Data por Yaser S. Abu-Mostafa
    Learning Spark: Lightning-Fast Big Data Analysis por Holden Karau
    Introduction to Data Mining por Pang-Ning Tan
    The Hundred-Page Machine Learning Book por Andriy Burkov
    Genetic Programming: An Introduction por Wolfgang Banzhaf
    Machine Learning: An Algorithmic Perspective por Stephen Marsland
    Machine Learning For Dummies por John Paul Mueller
    Principles of Data Mining por Max Bramer
    Practical Data Science with R por Nina Zumel
    Deep Learning: A Practitioner's Approach por Josh Patterson
    Data Mining with SQL Server 2005 por ZhaoHui Tang
    Complete Web Monitoring: Watching Your Visitors, Performance, Communities, and Competitors por Alistair Croll
    Machine Learning: The Art and Science of Algorithms that Make Sense of Data por Peter Flach
    Machine Learning for Absolute Beginners: A Plain English Introduction por Oliver Theobald
    Learning TensorFlow: A Guide to Building Deep Learning Systems por Tom Hope
    Web Analytics For Dummies por Pedro Sostre
    High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark por Holden Karau
    Mahout in Action por Sean Owen
    Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI por Darren Cook
    The Data Science Design Manual por Steven S. Skiena
    Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery por Graham Williams
    Agile Data Science: Building Data Analytics Applications with Hadoop por Russell Jurney
    Grokking Deep Learning por Andrew Trask
    Google Analytics por Justin Cutroni
    Deep Learning Cookbook: Practical Recipes to Get Started Quickly por Douwe Osinga
    Mastering Feature Engineering: Principles and Techniques for Data Scientists por Alice Zheng
    Practical Machine Learning: Innovations in Recommendation por Ted Dunning
    Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining por Glenn J. Myatt
    Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play por David Foster
    Text Mining: Predictive Methods for Analyzing Unstructured Information por Sholom Weiss
    Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning por Chris Albon
    Statistical Learning Theory por Vladimir N. Vapnik
    Non-standard parameter adaptation for exploratory data analysis por Wesam Ashour Barbakh
    Managing and Mining Graph Data por Charu C. Aggarwal
    Managing and Mining Uncertain Data por Charu C. Aggarwal