{ "media_type": "text", "post_content": "Physical book recommendations? \n\nLooking for topics on Python, ML, DS, etc. Any interesting must reads in the field?\n \nLooking heavy to up-skill and get more technical! The journey begins now.", "post_id": "6053cc88093ccd0022ed97d8", "reply_count": 10, "vote_count": 10, "bowl_id": "5ace5097be6e660013abcc42", "bowl_name": "Data & Analytics Consultants" }

Physical book recommendations? Looking for topics on Python, ML, DS, etc. Any interesting must reads in the field? Looking heavy to up-skill and get more technical! The journey begins now.

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Introduction to statistical learning Automate the boring stuff Hands on machine learning with scikit learn, keras and tensor flow Data science for business Data Smart

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Thanks!

End-to-end data science curriculum fit into one book: https://srdas.github.io/MLBook/ My very non-exhaustuve longer list... Most aren't ML. And not all are actually books. Easy: Machine Learning Yearning by Ng Google's ML style guide Hidden Technical Debt in Machine Learning Systems by Sculley et al (short paper) The Quick Python Book by Ceder (💤 but useful) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron How to Make Friends and Influence People by Carnegie Inspired by Cagan Everything on 3blue1brown youtube channel Harder: Designing Data Intensive Applications by Kleppman Linear Algebra Done Right by Axler ISL by James et al Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition? by Neilsen (masters thesis) Doing Bayesian Data Analysis by Kruschke Deep Learning by Goodfellow et al RL by Sutton and Barto Network Science by Barabási Harder harder: Statistical Inference by Casella and Berger ESL by Friedman, Tibshirani, Hastie BDA3 by Gelman et al

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ISL and the Geron book for hard skills, Inspired as a light read for how to work. Definitely read the Google ML guide and the tech debt paper. You can do both in under a day.

Highly recommend introduction to statistical learning. Doing the excercises in R in parallel will give you a broad understanding. Helped me a lot with my master thesis.

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Yes exactly. You will need some time end effort to go trough it. If you only want to know some fundamentals and simple coding, other books mitght be better suited. But if you want to really know eg how a random forest works in detail, the book is perfect. Nice balance between formulas, math, statistics and visualisations, explanations and examples.

Life 3.0

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For the Julia-curious among us, a draft hot off the digital press https://datasciencejuliahackers.com/

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