Machine Learning with TensorFlow, Python and… Azure!

The Machine Learning is now in a phase of continuous expansion, facilitated by the offers of all cloud platforms. In my first and second articles about this argument, we found out that a programmer can analyze data using high-level tools, even without a vast knowledge of statistics and machine learning. Presuming that everything going to work at the first attempt is quite unlikely, that is, we can build a model with our set of data with reasonable efficiency. In the last article about ML, I introduced the feature crossing idea, which leads us to come back to the manipulation of data. In this situation, high-level tools show their limits and make us search for new ones: they are such complicated that we are not able to handle options and wizards, which suddenly fail (I wrote about this problem in my second article).