bsletten

This is an incomplete, ever-changing curated list of content to assist people into the worlds of Data Science and Machine Learning. If you have a recommendation for something to add, please let me know. If something isn't here, it doesn't mean I don't recommend it, I just may not have had a chance to review it yet or not.

I will generally list things in order of easier to more formal/challenging content.

It may feel like there is an overwhelming amount of stuff for you to learn (because there is). But, there is a guided path that will get you there in time. You need to focus on Linear Algebra, Calculus, Statistics and probably Python (or R). Your best bet is to get a Safari Books Online account (https://www.safaribooksonline.com) which you may already have access to through school or work. If not, it is a reasonable way to get access to a tremendous number of books and videos.

I'm not saying you will get what you need out of everything here, but I have read/watched at least some of all of the following an

garybernhardt

This document has moved!

It's now here, in The Programmer's Compendium. The content is the same as before, but being part of the compendium means that it's actively maintained.

avibryant

An idiosyncratic guide to teaching yourself practical machine learning, without links:

• Find a binary classification dataset; maybe you have one internally.
• Implement a simple decision tree algorithm, like CART.
• Write some code to validate your model; produce an ROC curve and understand the tradeoff it embodies.
• Compare the ROC for your training set with the ROC for a holdout and understand what it means that they differ.
• Experiment with some hyperparameters: how does the comparison above change as you adjust the depth of the tree or other stopping criteria?
• Combine your decision tree algorithm with bagging to produce a random forest. How does its ROC compare?
• Do the same hyperparameter tuning here. (How many trees?) Reflect on overfitting and on the bias/variance tradeoff.

mjohnsullivan

// Updated example from http://rosettacode.org/wiki/Hello_world/Web_server#Rust
// to work with Rust 1.0 beta

use std::net::{TcpStream, TcpListener};
use std::io::{Read, Write};
use std::thread;


fn handle_read(mut stream: &TcpStream) {
    let mut buf = [0u8 ;4096];

acolyer

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

• http://mvdirona.com/jrh/talksandpapers/jamesrh_lisa.pdf

Basic tenets

• Does the design expect failures to happen regularly and handle them gracefully?
• Have we kept things as simple as possible?

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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