Launch - SEPTEMBER 20th | Now $4,000/ Regular Price $8,000
Your 8-Month AI Engineering Journey: A Project-Based Plan
This bootcamp is designed to turn you into a practical, hireable Machine Learning Engineer.
You will learn by building
Each month, you'll tackle new projects while iteratively developing a complex, multimodal AI assistant as your main capstone.
Prerequisites
- Python
- Python fundamentals
- Python data structures (lists, dictionaries, sets, tuples)
- List/dictionary comprehensions and functional programming tools
- OOP principles in Python (classes, inheritance, encapsulation)
- Virtual environments (venv, conda)
- Package management
- Git (branching, merging, pull requests)
- IDE (VSCode or another IDE of your preference)
- 15-20 hours/week commitment
If you are unfamiliar with some of these topics or want to refresh your knowledge,
the free resources linked below cover all prerequisites
the free resources linked below cover all prerequisites
- PythonIf you’re a complete beginner in programming, start with one of these:
- AI Python for Beginners course by DeepLearning.AI (~10h)
- Intro to Programming and Python courses by Kaggle (~10h)
- Python tutorial by Spark Code Hub
- A Byte of Python book by Swaroop Chitlur
- learnpython.org interactive tutorial by Ron Reiter
- Python for Beginners tutorial by Beau Carnes
- Object Oriented Programming with Python tutorial by JimShapedCoding
- Intermediate Python tutorial by Patrick Loeber
- Python Virtual Environments: A Primer by Martin Breuss
- GitFor a basic understanding, check one of these introductory resources:
- Git & GitHub Crash Course by Kalob Taulien (Udemy)
- GitHub and Git Tutorial for Beginners by Abid Ali Awan (DataCamp)
- Pro Git book by Scott Chacon and Ben Straub (check sections: Getting Started, Git Basics, Git Branching, Distributed Git, GitHub)
- Atlassian Git tutorials (check sections: Beginner, Getting started, Collaborating workflows)
- IDEOfficial tutorials for Visual Studio Code:
Core Continuous Project
Your Capstone in Development
Your Main (Capstone) Project: You will build the "Virtual Travel Explorer,"
an AI assistant that evolves each month. It starts as a simple script and finishes as a
deployed, tool-using, multimodal AI that can chat, recognize landmarks, and give real-time travel advice.
ML Portfolio Dashboard: Develop a personal portfolio website that showcases your projects and ML experiments. This grows throughout the course as you add new projects and visualizations.
COURSE SCHEDULE
Start: September 20th, 2025
2 webinars per week/ PST
2 webinars per week/ PST
- Wednesdays 7pm
- Saturdays 10am
SECTION 1: Classical ML & Tabular Data
Month 1: The Dawn of Intelligence: ML Foundations
Enter the ML universe and master the fundamentals
Mastering the data science toolkit, Exploratory data analysis (EDA), and core classical ML algorithms.
House Prices Prediction
- You'll build models to predict home values, mastering feature engineering and regression on a complex, real-world Kaggle dataset.
EDA, Pandas, Matplotlib, Scikit-learn, Linear Models, Kaggle Competitions.
Build the v0.1 command-line version with simple, rule-based responses.
Month 2: Classical ML Mastery & Unveiling Hidden Structures
Master advanced techniques and explore Unsupervised Learning algorithms
Iterative model improvement and discovering hidden patterns in data.
■ House Prices Prediction - Advanced Optimization
■ Credit Card Customer Segmentation
- You'll return to your house price predictor and improve it using advanced ensemble methods, sophisticated feature engineering, and hyperparameter optimization to climb the Kaggle leaderboard.
■ Credit Card Customer Segmentation
- You'll act as a data scientist for a bank, using clustering algorithms to find distinct marketing groups in a dataset of customer spending habits.
Decision Trees, Ensemble Methods, Stacking/Blending, Feature Engineering, Hyperparameter Optimization, K-Means, PCA.
v0.2, with a classical ML model to understand user travel intents.
SECTION 2: Neural Networks & Computer Vision
Month 3: Entering the Matrix: Neural Networks & Deep Learning Foundations
Build neural networks from scratch and unlock deep learning.
Building Neural Networks from fundamentals and applying to real
manufacturing problems.
manufacturing problems.
Fashion-MNIST Classifier - NN from Scratch
■ You'll demystify deep learning by building a Neural Network from
Scratch to classify images of clothing.
Fashion-MNIST Classifier - PyTorch CNN Upgrade
■ You'll upgrade your classifier to use PyTorch framework and see
performance improvement with Convolutional Neural Networks
(CNNs).
PCB (printed circuit board) Defect Classification
■ You'll tackle a real manufacturing problem by building a binary
■ You'll demystify deep learning by building a Neural Network from
Scratch to classify images of clothing.
Fashion-MNIST Classifier - PyTorch CNN Upgrade
■ You'll upgrade your classifier to use PyTorch framework and see
performance improvement with Convolutional Neural Networks
(CNNs).
PCB (printed circuit board) Defect Classification
■ You'll tackle a real manufacturing problem by building a binary
- classifier to detect defective vs normal PCBs.
Neural Network Fundamentals, Multi-Layer Perceptron (MLP),
Backpropagation, NumPy, PyTorch, Convolutional Neural Networks (CNNs), Transfer
Learning.
Backpropagation, NumPy, PyTorch, Convolutional Neural Networks (CNNs), Transfer
Learning.
v0.3, with a simple Neural Network to improve intent
understanding.
understanding.
Month 4: Where's Waldo? - Computer Vision Mastery
Master the complete computer vision pipeline
Training a high-performance object detector on your custom-labeled dataset and tackling a real-world medical image segmentation challenge.
PCB (printed circuit board) Defect Detection
■ You'll create your dataset by labeling PCB defects with bounding
boxes using CVAT, experiencing professional annotation workflows.
■ You'll train a YOLO model to localize and classify different defect types
on your custom dataset.
Medical Image Segmentation
■ You'll work on pixel-level computer vision by setting up and training a U-Net model for medical image segmentation.
■ You'll create your dataset by labeling PCB defects with bounding
boxes using CVAT, experiencing professional annotation workflows.
■ You'll train a YOLO model to localize and classify different defect types
on your custom dataset.
Medical Image Segmentation
■ You'll work on pixel-level computer vision by setting up and training a U-Net model for medical image segmentation.
Data Annotation, CVAT, Object Detection, YOLO Architecture, Image
Segmentation, U-Net Architecture.
Segmentation, U-Net Architecture.
v0.4, with a core visual feature to recognize famous places from
photos.
photos.
SECTION 3: NLP & LLMs
Month 5: Understanding Human Language
Bridge the gap between human language and AI understanding
Diving deep into modern NLP (Natural Language Processing) with
Transformer architectures.
Transformer architectures.
Comment Toxicity Detector
You'll build a system to moderate online discussions by fine-tuning a
Transformer to detect toxic comments.
You'll build a system to moderate online discussions by fine-tuning a
Transformer to detect toxic comments.
Natural Language Processing (NLP), Tokenization, Word Embeddings,
Attention Mechanisms, Transformer Architecture, Hugging Face.
Attention Mechanisms, Transformer Architecture, Hugging Face.
v0.5, with a powerful Natural Language Understanding (NLU)
Transformer.
Transformer.
Month 6: Rise of the Giants: The LLM Revolution
Harness the power of Large Language Models (LLMs) and modern AI
Building modern AI applications with Retrieval-Augmented Generation (RAG) and Parameter-Efficient Fine-Tuning (PEFT).
AI Trip Planner - RAG System
■ You'll build the core knowledge system for your main project. This
system will "read" real travel guides and answer complex,
conversational questions.
Function Calling Fine-Tuning
■ You'll dive into modern LLM customization, using the Low-Rank
Adaptation (LoRA) technique to teach an open-source LLM to become
a specialist at converting plain English questions into accurate
structured function calls for your travel agent.
■ You'll build the core knowledge system for your main project. This
system will "read" real travel guides and answer complex,
conversational questions.
Function Calling Fine-Tuning
■ You'll dive into modern LLM customization, using the Low-Rank
Adaptation (LoRA) technique to teach an open-source LLM to become
a specialist at converting plain English questions into accurate
structured function calls for your travel agent.
RAG, LangChain, Vector Databases, LLM APIs, LLM Fine-Tuning, LoRA,
PEFT, Function Calling, Prompt Engineering, Agent Development.
PEFT, Function Calling, Prompt Engineering, Agent Development.
v0.6, with a powerful LLM brain and the RAG system for
providing deep travel knowledge.
providing deep travel knowledge.
SECTION 4: Advanced Topics
Month 7: The Oracle, The Agent, and The Architect
Conquer temporal data, try AI agents, and explore MLOps practices
Mastering time series forecasting and learning the essential MLOps skills and tools.
Store Sales Time Series Forecasting
■ You'll tackle a classic business problem by predicting future sales for a retail chain, mastering temporal feature engineering, and comparing
various forecasting models.
Building an AI Agent - The Tool-User
■ You will enhance your "Virtual Travel Explorer" with its first agentic
capability.
MLOps in Practice - Deploying a Model
■ You'll create
■ You'll tackle a classic business problem by predicting future sales for a retail chain, mastering temporal feature engineering, and comparing
various forecasting models.
Building an AI Agent - The Tool-User
■ You will enhance your "Virtual Travel Explorer" with its first agentic
capability.
MLOps in Practice - Deploying a Model
■ You'll create
Time Series Analysis, Forecasting, Prophet, LSTMs, Agents, ReAct,
LangChain, MLOps, CI/CD for ML pipelines.
LangChain, MLOps, CI/CD for ML pipelines.
v0.7, now an agent that can use tools (like a Weather API) and is
deployed locally.
deployed locally.
Month 8: Polishing Your Armor: Capstone & Career Launch
Perfect your portfolio and launch your AI engineering career
Synthesizing all your skills into a flagship portfolio project and preparing for the job market.
Virtual Travel Explorer v1.0 (Capstone)
■ You will build a polished, multimodal AI assistant that can chat,
recognize landmarks, use a RAG knowledge base, and connect to live
APIs. This is your flagship portfolio piece.
ML Portfolio Dashboard Finalization■ You'll finalize and polish your personal portfolio website that has grown
throughout the course.
■ You will build a polished, multimodal AI assistant that can chat,
recognize landmarks, use a RAG knowledge base, and connect to live
APIs. This is your flagship portfolio piece.
ML Portfolio Dashboard Finalization■ You'll finalize and polish your personal portfolio website that has grown
throughout the course.
System Integration, Productization.
You will finalize your portfolio, polish your resume, practice ML system design questions, and conduct mock interviews to get you ready to be hired.
🗓️ Month 1: Tabular Data Mastery & Foundations
- Focus: Mastering the data science toolkit, EDA, and core regression models.
- Project: House Prices - Advanced Regression Techniques
- You'll build models to predict home values, mastering feature engineering and regression on a complex, real-world Kaggle dataset.
- (Skills: EDA, Feature Engineering, Regression Models, XGBoost, Kaggle Competitions)
Main Project Goal: Build the v0.1 command-line version with simple, rule-based responses.
Our YouTube Channel
Learn from the Experts
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subscribers
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We believe in sharing knowledge, so on our channel, you'll find:
Student success stories
Tutorials and useful materials for aspiring QA engineers
Tips on interviews and job opportunities in the QA field
With over 1 million views, our channel has become a trusted resource for learning and growth in the QA industry.
Student Success Stories
Nothing speaks louder than real success stories. Our students come from different backgrounds, but they all have one thing in common — they successfully transitioned into QA careers after completing our program.
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