At the end of the 3-month educational program, you enter the second phase of our placement program, where we help you build a solid resume and connect you with companies for your first interviews.

Once you accept the job offer, we will help you apply for a special work visa for Mexicans.

In addition, we offer housing to ease your transition from Mexico to Silicon Valley.

We also have the option of working in Mexico.

Curso en en-linea y en vivo con instructor, los Sábados de 12:00PM a 4:00PM.

They work in weekly periods, developing projects based on Ai. In each class the results of the projects are evaluated and new methodologies of use are shown. At the end of the course a final exam is presented which qualifies aptitudes and programming level. This information is vital for the creation of Curriculum.

Our team of instructors is made up of programmers who currently work for technology companies such as: Facebook, Google and Amazon.

Start date: January 2023

5 meses
1 class per week (3 hours)
Saturday from 12:00 PM to 3:00 PM
Horas totales: 48 Horas
Location: Online (via zoom)
Limited space.

Program Investment:

More than a course, Ai Lab School is an investment in your growth.

Investment of the program is 33,000 MXN. Ask about our scholarship and financing plan. There are scholarships of up to 50% plus payment options once we place you in a job.

Registration fee is 12,000 MXN. Make your reservation with 2,000 MXN.

* We have 2 forms of payment:

1) Paga con trabajo desarrollando tu portafolio de Artificial Intelligence

2) Pay in installments

For more information about our payment plan schedule a video call with a counselor: https://calendly.com/ailabschool/30min

A preliminary interview is conducted to evaluate the economic status of the applicant and determine the scholarship percentage.

The amount of the program can be paid with the different options we offer:

• Pay with work at Ai Lab School
• Pay in installments

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.