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  • machine-learning

  • prework

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Introduction to Machine Learning

How prepared will you be after this course?
Prework syllabus in a nutshell
  • Prework

You are here because you want to become a Machine Learning expert. What an intelligent decision 🤯!

I don't have to tell you how AI is already shaping our world in ways that are impossible to explain. Almost every company uses AI nowadays: Every army in the world, airlines, streaming sites, all big companies use AI to predict and make better decisions.

How prepared will you be after this course?

Just as an example, here is a quick taste of what you will be capable of doing:

Note: Some of these example projects were developed by our graduates.

  1. Diagnost pneumonia by looking at x-rays with 90% accuracy (higher than doctors).
  2. Use AI to automatically find a missing child in 20k people crowds (this happened for real).
  3. Predict stock market prices with 70%+ accuracy (for specific scenarios).
  4. Find people holding guns in crowded places, like airports, concerts, etc.

These examples are just the tip of the iceberg. You will acquire knowledge during the course that will make you a perfect fit into most AI teams in the world; help you shape and develop today's most advanced Artificial Intelligence.

Looking for jobs

Today, our students are already working at companies like CEMEX, Blackstone, the City of Coral Gables (the most advanced city in the US in terms of AI), and many others.

You won't need any other courses or training; all you have to do is work hard to practice what you learned and demonstrate the same skills you acquired in this course during your next job interview.

Note: Some of our graduates take up to 180 days to find a job in AI. The journey is different for everyone; it's up to you to take this challenge and work hard until it pays off. We will be with you during the whole process. You can do this!


The following are our four cornerstones applied to any of our courses:

  1. Flipped classroom: The class will be a lab; you will come to class with lessons/videos already read and lots of questions. Ready to practice! The only way to learn technical skills is through practice.
  2. Ask questions: The course was built in a way that you will need to ask questions; we emphasize this intensively because it has become an essential skill for today's agile and technical teams; writing a question "the right way" helps you structure your ideas logically, enables you to understand better, lets you be better at "googling", and it's also the fastest way to stop being stuck, which is the #1 barrier for learning a technical skill like this.
  3. A little every day is better than a lot just one day: The brain forgets 80% of what it learns after seven days. That's why it's so important to practice every day. The course is designed for consistent practice; missing two classes in a row will put you in a very challenging position, it will be difficult to recover those days.
  4. Focus: Let your family and friends know about this course; ask them to let you focus and study. The following 16 weeks will change your professional career forever, and having the right mindset is essential for a successful experience.

Prework syllabus in a nutshell

The Prework is just the first module of the course: An Introduction to Python and Data Science.

In general terms, we will cover the following topics; please note that this table of contents is not written in stone and is subject to change without notice; our only goal is to give you a general idea of what to expect:


  1. Coding in Python: Loops, Functions, Classes & Objects.
  2. Most used libraries: NumPy, Pandas & Matplotlib.
  3. Working with files.
  4. Working with the terminal.
  5. Introduction to Data Science.
    5.1. Types of data: numerical, categorical, ordinal.
    5.2. Types of averages: mean, median, mode.
    5.3. Types of spread.
    5.4. Quantile vs. Percentile.
    5.5. Data visualization:
    • 5.5.1. One variable: Histogram, Bar Plot, Pie Chart.
    • 5.5.2. Two variables: Scatter Plot, Line Plot, 2D Histogram.
    • 5.5.3. Three variables: Heat Map, Multivar Line Plot.