190 universities just launched 600 free online courses. Here’s the full list

If you haven’t heard, universities around the world are offering their courses online for free (or at least partially free). These courses are collectively called MOOCs or Massive Open Online Courses.

In the past six years or so, over 800 universities have created more than 10,000 of these MOOCs. And I’ve been keeping track of these MOOCs the entire time over at Class Central, ever since they rose to prominence.

In the past four months alone, 190 universities have announced 600 such free online courses. I’ve compiled a list of them and categorized them according to the following subjects: Computer Science, Mathematics, Programming, Data Science, Humanities, Social Sciences, Education & Teaching, Health & Medicine, Business, Personal Development, Engineering, Art & Design, and finally Science.

If you have trouble figuring out how to signup for Coursera courses for free, don’t worry — here’s an article on how to do that, too.

Many of these are completely self-paced, so you can start taking them at your convenience.

https://qz.com/1437623/600-free-online-courses-you-can-take-from-universities-worldwide/

Practical Text Classification With Python and Keras

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.

Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this?

https://realpython.com/python-keras-text-classification/

A visual introduction to machine learning

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Model Tuning and
the Bias-Variance Tradeoff

The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex.

http://www.r2d3.us/visual-intro-to-machine-learning-part-2/

Face recognition with OpenCV, Python, and deep learning

In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:

  • OpenCV
  • Python
  • Deep learning

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!

https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/

AWS DeepLens: first impressions + tutorial

Getting up-and-running with Amazon’s new machine learning-enabled camera

tl;dr It’s awesome. Get one.

At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.

On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.

https://medium.com/@CUlstrup/aws-deeplens-first-impressions-tutorial-17e6d448d58d

Machine Learning on AWS

Why machine learning on AWS?

Machine Learning for everyone

Whether you are a data scientist, ML researcher, or developer, AWS offers machine learning services and tools tailored to meet your needs and level of expertise.

API-driven ML services

Developers can easily add intelligence to any application with a diverse selection of pre-trained services that provide computer vision, speech, language analysis, and chatbot functionality.

Broad framework support

AWS supports all the major machine learning frameworks, including TensorFlow, Caffe2, and Apache MXNet, so that you can bring or develop any model you choose.

Breadth of compute options

AWS offers a broad array of compute options for training and inference with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs.

Deep platform integrations

ML services are deeply integrated with the rest of the platform including the data lake and database tools you need to run ML workloads. A data lake on AWS gives you access to the most complete platform for big data.

Comprehensive analytics

Choose from a comprehensive set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, data workflow orchestration.

Secure

Control access to resources with granular permission policies. Storage and database services offer strong encryption to keep your data secure. Flexible key management options allow you to choose whether you or AWS will manage the encryption keys.

Pay-as-you-go

Consume services as you need them and only for the period you use them. AWS pricing has no upfront fees, termination penalties, or long term contracts. The AWS Free Tier helps you get started with AWS.

A Concrete Introduction to Probability (using Python)

This notebook covers the basics of probability theory, with Python 3 implementations. (You should have some background in probability and Python.)

In 1814, Pierre-Simon Laplace wrote:

Probability … is thus simply a fraction whose numerator is the number of favorable cases and whose denominator is the number of all the cases possible … when nothing leads us to expect that any one of these cases should occur more than any other.

Laplace

Pierre-Simon Laplace
1814

Laplace really nailed it, way back then! If you want to untangle a probability problem, all you have to do is be methodical about defining exactly what the cases are, and then careful in counting the number of favorable and total cases. We’ll start being methodical by defining some vocabulary:

  • Experiment: An occurrence with an uncertain outcome that we can observe.
    For example, rolling a die.
  • Outcome: The result of an experiment; one particular state of the world. What Laplace calls a “case.”
    For example: 4.
  • Sample Space: The set of all possible outcomes for the experiment.
    For example, {1, 2, 3, 4, 5, 6}.
  • Event: A subset of possible outcomes that together have some property we are interested in.
    For example, the event “even die roll” is the set of outcomes {2, 4, 6}.
  • Probability: As Laplace said, the probability of an event with respect to a sample space is the number of favorable cases (outcomes from the sample space that are in the event) divided by the total number of cases in the sample space. (This assumes that all outcomes in the sample space are equally likely.) Since it is a ratio, probability will always be a number between 0 (representing an impossible event) and 1 (representing a certain event).
    For example, the probability of an even die roll is 3/6 = 1/2.

This notebook will develop all these concepts; I also have a second part that covers paradoxes in Probability Theory.

http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb