I've been regularly monitoring the machine learning job postings on upwork.com and absolutely love some of the requests coming through, especially those focusing on stock picking or FX (forex / currency exchange) prediction.  And they want it cheap.  Which begs the question that if you had the ability to create a learning algorithm that could make stock predictions and you could make money on it, would you really sell it to someone for $100?

Here's a typical one:

I recently stumbled on The Joel Test: 12 Steps to Better Code while perusing a job ad:

The Joel Test

Nice, quick demo on using both Watson and Tensorflow in comparing three images:

https://steemit.com/programming/@cristi/battle-of-learning-machines-watson-vs-tensorflow-on-image-classification

In my udacity machine learning class I was confronted with this list of Supervised Learning Models (lots of information here) from scikit-learn:

• Gaussian Naive Bayes (GaussianNB)
• Decision Trees
• Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)
• K-Nearest Neighbors (KNeighbors)
• Stochastic Gradient Descent Classifier (SGDC)
• Support Vector Machines (SVM)
• Logistic Regression

First set of results.  More on the way. 

Update (2017-05-08)

Still looking and found there is quite an abundance:

https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public

https://blog.bigml.com/list-of-public-data-sources-fit-for-machine-learning/

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Here are list of datasets I have been accessing for training and testing my machine learning algorithms.