Machine Learning: Instructional Materials

Chapter 1: Bird's Eye View of Applied Machine Learning - Data Science Primer


Welcome to our 7-part mini-course on data science and applied machine learning! Over these 7 chapters, our goal is to provide you with an end-to-end blueprint for applied machine learning, while keeping this as actionable and succinct as possible. With that, let's get started with a bird's eye view of the machine learning workflow. One really cool (optional) challenge you can do in the next hour is training your first machine learning model! That's right, we've put together a complete step-by-step tutorial for training a model that can predict wine quality.

Best deals for Nov 7: Save on Apple MacBook Air, AirPods, Instant Pot, iPad Mini 4, Microsoft Surface Book 2, and more


We hope you have your holiday shopping game face on because the details today are pretty excellent. We're rounding up the best deals from Amazon, Walmart, Best Buy, and Macy's on Apple products, laptops and accessories, kitchen appliances, and even Amazon's own devices. We're also highlighting deals on Udemy online classes in case you feel inspired to learn a little something. There are a number of Apple products on sale, such as the Apple MacBook (mid-2017) 12-inch laptop, which is priced at $999.99, or $400 off its list price. It seems that the previous generation is on sale now before the new MacBook Air 2018 model hits store shelves .

A Gentle Introduction to LSTM Autoencoders


An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. In this post, you will discover the LSTM Autoencoder model and how to implement it in Python using Keras. A Gentle Introduction to LSTM Autoencoders Photo by Ken Lund, some rights reserved. An autoencoder is a neural network model that seeks to learn a compressed representation of an input.

Machine learning with Python: Essential hacks and tricks


It's never been easier to get started with machine learning. In addition to structured massive open online courses (MOOCs), there are a huge number of incredible, free resources available around the web. Here are a few that have helped me. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems.



In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class. As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relevant to the field. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn't help them at all in using ML for their own purposes.

18 Best Artificial Intelligence Courses To Standout in The Future JA Directives


Looking for Artificial Intelligence Tutorial to learn introduction to artificial intelligence? Grab the list of Best Artificial Intelligence Courses Online, Tutorials, and Training are offered by a number of massive open online course (MOOC) providers like Udemy, Coursera, and edX. Artificial Intelligence (AI) and machine intelligence are the most booming topics in every industry now. Some of this popular MOOC providers offer some in-depth artificial intelligence programs. The list of the Best Artificial Intelligence Certification is often taught by industry top AI researchers or experts and you will learn the best applications of artificial intelligence.

TensorFlow: Building Feed-Forward Neural Networks Step-by-Step


In this article, two basic feed-forward neural networks (FFNNs) will be created using TensorFlow deep learning library in Python. The reader should have basic understanding of how neural networks work and its concepts in order to apply them programmatically. This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details. Before actual building of the neural network, some preliminary steps are recommended to be discussed. Here is the first classification problem that we are to solve using neural network.

A gentle introduction to relational learning


When big data is really small Machine Learning for all, what does it mean to democratize AI? A simple example Resources 5 7. 7 The last 40 years have witnessed massive adoption of the relational model It's hard to find any examples today of enterprises whose data isn't in a relational database Millions of human hours invested in building relational models and populating them with data Relational databases are rich with knowledge of the underlying domains that they model The availability and accuracy of large amounts of curated data has made it possible for humans (BI) and machines (AI) to learn from the past and to predict the future The relational model dominates data management 8. When big data is small 9. 9 What would a database do? Features Entities 2. Feature extraction query s: Aggregates (statistics) generated from model spec and feature extraction query 3. Model specification (e.g., "degree 2 ridge regression") 1. Database ID x 1 x 2 x 3 ... y 10. 1 0 Supported methods include Linear regression Polynomial regression Factorization machines Decision trees Linear SVM K-Means & K-Median clustering Principal component analysis Deep sum-product networks (with more on the way) Does it work for all model classes or methods?