1. Introduction to pandas and numpy

2. Time series, Azure data factory

3. ML with Python

4. Using sagemaker for modeling and implementation

1. Artificial intelligence para principiantes: Aplicaciones y Alcances

1. Artificial intelligence
2. Machine learning
3. Deep learning

Introduction to linear classification

1. Design of a simple binary classifier

3. Introduction to artificial neural networks

1. Artificial neurons: Brief history and basic theory
2. Perceptron: Mathematical fundamentals and implementation
3. Adaptive linear neurons: Mathematical foundations and implementation.

4. Densely connected neural networks

1. Underlying mathematical theory
2. Implementation of a densely connected neural network to classify images of handwritten digits belonging to the MNIST ("Hello World") database.

5. Convolutional Neural Networks 

1. Basic theory
2. Implementation of a convolutional neural network in a biometric recognition problem.
3. Transfer of learning
4. Data enhancement
5.  Deployment of a convolutional neural network model in a web app.
6. Web service creation using Flask
7.  Web app configuration
8.  Encoder-decoder type architectures
   1.  Basic theory
   2.  UNet Architecture
   3.  Implementation of the UNet architecture in a semantic segmentation problem.

6. Recurrent neural networks and their use in sequential data processing.

1. Basic theory
2. Implementation of recurrent neural networks to address sentiment analysis and temperature prediction problems.

7. Antagonistic generative networks

1. Basic theory
2. Implementation of an antagonistic generative network to generate synthetic data.