AI Engineering

This is where your data science journey begins! You’ll get hands-on with Python, the go-to language for data science. You’ll also master Git and GitHub, essential tools for managing your code and collaborating with others.

Never used the command line before? No problem! We’ll walk you through the basics of Unix commands, helping you navigate files, automate tasks, and streamline your daily workflows. This phase is your foundation for everything ahead – from data analysis to machine learning!

In this phase, you’ll learn how to analyze and prepare data efficiently. With Pandas, you’ll tackle large datasets, clean them, and reshape them to find meaningful insights.

We’ll also cover SQL, a crucial skill for any data professional. You’ll learn how to query databases to extract the information you need to make data-driven decisions. And of course, we’ll help you create beautiful visualizations using Matplotlib and Seaborn to showcase your results.

This phase wraps up with a hands-on project where you’ll conduct a fictional business analysis – just like you would in a real job!

In this phase, you’ll dive into Supervised Learning and understand how machines learn from data.

You’ll explore regression and classification models, two essential tools in the data scientist’s toolkit. Whether you’re predicting future trends or categorizing data, these models are at the core of modern data science.

You’ll also learn how to evaluate your models using metrics like precision, accuracy, and F1 score. By the end of this phase, you’ll have the skills to build and assess your own machine learning project.

You’ll explore Deep Learning algorithms - starting with neural networks -, l learn how they work and how they can process images, text, and more.

A key highlight is Natural Language Processing (NLP) – how machines understand and process human language. From chatbots to voice assistants, this is where you amp up your game.

We’ll also introduce you to Recommender Systems, the tools behind platforms like Netflix and Spotify.

Bring your machine learning models into production. You’ll create APIs and containerize your models with Docker.

Cover cloud deployment, making your solutions scalable and secure in a real-world environment - with the goal that your model doesn't just, but runs reliably in production.

To wrap up your bootcamp, you’ll put everything you’ve learned into practice with a Capstone Project. You’ll develop your own machine learning solution, deploy it, and ensure it stays reliable over time.