The Machine Learning Fundamentals Bootcamp!
Get your Machine Learning Career Started!
I am glad to introduce the Machine Learning Fundamentals Bootcamp. On February 8th, 2024, I am going to start training candidates who aspire to transition toward a Machine Learning career. This is going to be 7 weeks of intense hands-on learning, concluding with resume writing and interview tips. We are going to span to the following subjects:
Machine Learning System Design
Traditional Machine Learning
Deep Reinforcement Learning
The Bootcamp is going to be limited to 30 candidates and early-bird pricing ($1200) will last until next week. Make sure to register before it is too late:
Members of the AiEdge Newsletter get a 20% discount by applying the following coupon: NEWSLETTER.
Who is this Bootcamp for?
This Bootcamp is meant for professionals or students who want to get their career started in the world of Machine Learning. You don't have to have any experience in the field to succeed in this Bootcamp, but you should have a strong interest and be ready to learn on your own if you find yourself stuck. This Bootcamp is perfect if you are a software engineer who wants to transition to ML.
Be ready to learn!
This Bootcamp is not meant to be easy! Be ready to spend time and effort in learning the subject so that the certificate means something.
I won't promise you that you will get a job after graduating (because it depends on you), but I can promise you that your understanding of Machine Learning will be at a completely different level!
Proficiency in Python - at least 6 months experience.
Comfortable with mathematical notation - at least 1st year college level in mathematics.
What is included!
39 hours of live classes
6 homework projects
Certification upon graduation
Access to our online community
Our first class will start at 9 AM PST on February 8th, 2024. We will meet twice a week on Thursday and Friday. If you cannot join, don't worry; every session will be recorded.
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.