The Machine Learning Fundamentals BootCamp

7 Weeks of Intense Learning!

Machine Learning System Design (1 week)

Machine Learning (ML) system design is crucial for building effective ML solutions. It involves structuring the entire ML project to align with specific goals, ensuring efficiency, scalability, and performance. Proper design is key for integrating data handling, model training, and deployment while addressing real-world complexities like data variability and scalability.

ML system design is possibly the most important skill for becoming a machine learning engineer. Being able to effectively architect a viable ML solution to bring value to customers is what makes the difference between success and failure! We are going to focus on the key aspects of managing and designing ML solutions.

Fundamental of Machine Learning (2 weeks)

If one wants to become an ML engineer, honing one's skills in traditional machine learning remains critical! Most ML projects start with non-deep learning models. To this day, XGBoost remains the most used and best-performing model on tabular data, but what makes it special?

There are literally thousands of supervised learning algorithms, so instead of focusing on outdated or underperforming models, we are going to dive into the inner workings of models like XGBoost, LightGBM, and CatBoost to understand why they took the crown when it comes to statistical learning.

Once we understand how the algorithms work, we are going to focus on how to automate the training of ML models. Every ML engineer should aim to automate the development, validation, and deployment of their ML models!

Deep Learning (2 weeks)

It is not possible nowadays to be an ML engineer without having an intimate understanding of Deep Learning techniques! Deep Learning revolutionized the fields of computer vision, natural language processing, recommender systems, and many others.

More than any other domain in ML, deep learning requires the mind of an architect. We are going to dissect the different foundational model units and how to use them to build large models, and we are going to learn how to architect the right loss function for the right learning problem.

We are going to dive into the specialized architectures and how we can use them for different applications.

Deep Reinforcement Learning (1 week)

For a long time, Reinforcement Learning was just an academic curiosity. Deep Learning completely changed that! It is now becoming one of the fastest emerging ML domains with applications such as autonomous driving and generative AI.

We are going to dive into the different strategies to train a model to make human-like decisions. We are going to focus on how Deep Reinforcement Learning can be used to fine-tune Large Language Models (LLMs).

Career Coaching (1 week)

Being good at interviewing has nothing to do with being on the job! We are going to look at the best strategies to apply for jobs, design powerful resumes, and how to prepare for interviews.