# Udemy Blowout! ANY course for $10 (not just mine) October 27, 2016 Udemy brings the good news to me, and I bring it to you. From Oct 26, 2016 at 11:59PM until November 1 at at 6:00AM (PST), all courses on Udemy will be only$10 if you click the link below:

http://bit.ly/2exC0Db

Enjoy!

# How to setup a custom domain with SSL and Medium.com with CloudFlare

October 27, 2016

This short tutorial will show you how to setup a custom domain with SSL and Medium.com with CloudFlare.

To start, you of course must own the domain, let’s call it example.com.

Suppose you want your Medium blog to have the address blog.example.com, to differentiate it between your main site.

If you have your DNS configured to point to CloudFlare (highly recommended so traffic doesn’t always hit your server directly), then it’s a little trickier than Medium.com’s default instructions, which are posted here:

https://help.medium.com/hc/en-us/articles/213474588-How-do-I-set-up-a-custom-domain-

As stated in the article, the first step is to fill out a form, and someone from the support team will get back to you with configuration details.

When you get a response, login to your CloudFlare account and click the DNS tab:

There are 2 types of records you have to add, CNAME and A Records.

You will get a name-value pair that looks like:

<token>.blog.example.com

And

<token2>.comodoca.com

The email will tell you that you can enter the name as <token>.blog.example.com OR just <token>.blog. I have found the latter to work.

You will enter the CNAME record as follows:

Finally, you will receive a list of A Records to add. These should be added under the “Value” column. The “Name” column should just be “blog”.

And that’s it! Easy peasy.

# Announcing Data Science: Supervised Machine Learning in Python (Less Math, More Action!)

September 16, 2016

In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now “machine learning first”, meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

The best part about this course is that it requires WAY less math than my usual courses; just some basic probability and geometry, no calculus!

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.

It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=EARLYBIRDSITE

UPDATE: New coupon if the above is sold out:

https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=SLOWBIRD_SITE

#data science #machine learning #matplotlib #numpy #pandas #python

# How to get ANY course on Udemy for $10 for the next week August 25, 2016 For some reason Udemy announced a promotion but when you go to the site it doesn’t appear. Just use this link to get ANY course on Udemy for$10:

http://bit.ly/2byIkWW

# New course – Natural Language Processing: Deep Learning in Python part 6

August 9, 2016

[Scroll to the bottom for the early bird discount if you already know what this course is about]

In this course we are going to look at advanced NLP using deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

• king – man = queen – woman
• France – Paris = England – London
• December – Novemeber = July – June

We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

See you in class!

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=EARLYBIRDSITE

UPDATE: New coupon if the above is sold out:

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=SLOWBIRD_SITE

#deep learning #GLoVe #natural language processing #nlp #python #recursive neural networks #tensorflow #theano #word2vec