AI & Machine Learning
The rigorous roadmap to breaking into Artificial Intelligence. From basic math and data manipulation to building Large Language Models (LLMs) and Generative AI systems.
Mathematics & Statistics
RequiredAI is just math under the hood. You don't need a PhD, but you absolutely must understand the core concepts to know how models learn and optimize. Focus on Linear Algebra for matrix operations, Calculus for gradient descent, and Probability/Statistics for distributions and evaluation metrics.
Python Programming
RequiredPython is the undisputed king of AI. You must master Python scripting, object-oriented programming, and functional paradigms to write clean data pipelines.
Data Analysis & Visualization
RequiredBefore you train a model, you must understand your data. Data scientists spend 80% of their time cleaning and visualizing data. Master NumPy for high-performance arrays, Pandas for tabular data manipulation, and Matplotlib/Seaborn for plotting charts.
Classic Machine Learning
RequiredNot every problem requires a massive Neural Network. Learn the classic algorithms that power most business AI today. Understand Regression (predicting numbers), Classification (categorizing data), Clustering, and Decision Trees using Scikit-Learn.
Deep Learning & Neural Networks
RequiredWelcome to modern AI. You will learn the architecture of Neural Networks, how backpropagation works, and how to build models for Computer Vision (CNNs) and Sequence data (RNNs).
Generative AI & LLMs
RecommendedThe cutting edge of technology. Learn how Large Language Models like GPT work (Transformers), how to fine-tune them on custom data, and how to orchestrate them using APIs.
MLOps
RecommendedTraining a model in a Jupyter Notebook is easy. Deploying it to production at scale is hard. Learn how to package, deploy, and monitor machine learning models in the real world.