This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.

Want to watch this again later? Sign in to add this video to a playlist. Report Need to report the video? Sign in to report inappropriate content. Report Need to report the video?

Here's an intro to techniques you can use to represent your features - including Bucketing, Crossing, Hashing, and Embedding - and utilities TensorFlow provides to help. Also included is a walkthrough of using TensorFlow Estimators to classify structured data. You can find Josh on Twitter: https://twitter.com/random_forests See Josh as a guest speaker in Week 2 of the openSAP course: https://goo.gl/UGGcX7 Check out more Machine Learning Recipes here: https://goo.gl/KewA03

It's time to write our first classifier. This is a milestone if you're new to machine learning. We'll start with our code from episode #4 and comment out the classifier we imported.

Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I'll walk you through writing a Decision Tree classifier from scratch, in pure Python. I'll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr

Last time we wrote an image classifier using TensorFlow for Poets. This time, we'll write a basic one using TF.Learn. To make it easier for you to try this out, I wrote a Jupyter Notebook for this episode -- https://goo.gl/NNlMNu This is a great way to get all the dependencies installed and properly configured. I've linked some additional notebooks below you can try out, too.

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.

Rich Sutton is old school king of RL.

Six lines of code is all it takes to write your first Machine Learning program. My name's Josh Gordon, and today I'll walk you through writing Hello World for Machine learning. In the first few episodes of the series, we'll teach you how to get started with Machine Learning from scratch. To do that, we'll work with two open source libraries, scikit-learn and TensorFlow. We'll see scikit in action in a minute. But first, let's talk quickly about what Machine Learning is and why it's important.

Udacity 2,479 views What is machine learning?