### Description

Math is a crucial skill for people who are interested in Data Science and Machine Learning. Until now, most of the people who are doing Data Science have a strong background in math, usually, people with master or Ph.D. degrees.

However, this fact seems to change in the next years, after the hype of
Machine Learning we are facing a process of *democratization*. Now the
door of Data Science is open for everyone.

To *truly madly deeply* understand how the machine learning algorithms
work we need to understand some mathematical concepts. In this tutorial,
I would like to share my experience in the process of learning some of
those concepts.

What I want to do is build a bridge between those concepts and Python,
more specifically, *SciPy* and *NumPy* and *TensorFlow*. Basically is
*just another tutorial about vectorization* , in this case, oriented to
understand and implement machine learning algorithms and the
mathematical foundation that supports it.

#### Outline

*Review of linear algebra*: A brief review of essential*linear algebra*, concepts and the explanation with NumPy: scalars, vectors, matrices, tensors, multiplication of vectors, inner products, vector spaces, etc.*Review of machine learning*: A brief review of essential*machine learning*, concepts that are related to mathematical concepts: dimensionality reduction, principal component analysis, etc.*Optimization*: Analytical vs. numerical solutions. This part has too many formulas,*well, not too many*.*Vectorization*: Numpy and TensorFlow.- Small practical cases:
- I want to show how we can solve the XOR problem with only one neuron
- Explain the math behind linear regression.

The material of the talk can be found here