AI Engineering Roadmap

Linear Algebra Fundamentals for Machine Learning

Calculus Fundamentals for Machine Learning

Probability & Statistics Fundamentals for Machine Learning

Introduction to Databases

SQL for Data Science Fundamentals

Getting Started with Git

Core ML & Data Science

Introduction to Data Science

Introduction to Machine Learning

Fundamentals of Model Evaluation and Metrics

Introduction to Data Cleaning and Preprocessing

Essential Numpy and Pandas

Data Visualization with Matplotlib and Seaborn

Introduction to Exploratory Data Analysis

Getting Started with Scikit-Learn

Introduction to Multimodal AI

Introduction to Autoencoders and Feature Learning

Applied Autoencoders for Feature Extraction

Introduction to AI & LLMs

Introduction to Large Language Models

Understanding LLM Model Sizes and Hardware Requirements

Getting Started with Local LLMs

Introduction to Transformer Models

Introduction to LLM Red Teaming

Stage 2: Applied AI

Get into deep learning, develop LLM applications, and learn the basics of ML engineering.

Deep Learning Specialization

Introduction to Neural Networks

Introduction to Deep Learning

Deep Learning Fundamentals with Keras

Getting Started with TensorFlow

Getting Started with PyTorch

Getting Started with JAX

Recurrent Neural Networks and Sequence Modeling

Model Regularization and Optimization in Deep Learning

PyTorch for TensorFlow Developers

LLM & Generative AI Development

Prompt Engineering and LLM Application Development

Getting Started with Retrieve-Augmented Generation (RAG)

Python for LLM Workflows: Tooling and Best Practices

Introduction to Diffusion Models for Generative AI

Introduction to Synthetic Data for Machine Learning

Vector Databases and Semantic Search Implementation

Synthetic Data for LLM Pretraining and Fine-Tuning

ML Engineering & MLOps Basics

Introduction to Data Engineering

Introduction to ETL Pipelines

Data Structures and Algorithms for Machine Learning

Docker and Containerization for ML Projects

FastAPI for ML Model Deployment

Introduction to Machine Learning Deployment

Data Versioning and Experiment Tracking for Machine Learning

Model Interpretability with SHAP and LIME

Cloud Platforms for Machine Learning: AWS, GCP, and Azure

Time Series Analysis and Forecasting

Stage 3: Advanced AI Systems

Master advanced LLM techniques, explore specialized AI applications, and scale systems with production MLOps.

Advanced LLM Techniques

Fine-tuning and Adapting Large Language Models

Advanced LoRA and PEFT Techniques for LLM Fine-Tuning

RLHF: Reinforcement Learning from Human Feedback

LangChain for Production-Ready LLM Applications

Advanced Vector Search for LLM Applications

How To Build A Large Language Model

Optimizing RAG Systems for Production Environments

Practical Quantization for Large Language Models

Specialized AI Applications

Introduction to Computer Vision

Advanced CNNs for Computer Vision Applications

Natural Language Processing Fundamentals

Introduction to Reinforcement Learning

Intermediate Reinforcement Learning Techniques

Advanced Reinforcement Learning Techniques

Advanced Transformer Architecture

Autoencoders and Representation Learning

Variational Autoencoders: Advanced Techniques and Representation Learning

Advanced Generative Adversarial Networks

Advanced Graph Neural Networks: Architectures and Implementation

Advanced Speech Recognition and Synthesis

Advanced Diffusion Model Architectures and Training

Advanced Synthetic Data Generation: GANs and Diffusion Models

Evaluating Synthetic Data Quality: Advanced Techniques

Production MLOps & Scalability

Monitoring and Managing ML Models in Production

MLOps for Large Models (LLMOps)

Advanced Feature Store Implementation for ML Systems

Deploying Quantized LLMs for Efficient Inference

Deploying Diffusion Models at Scale

Advanced TensorFlow Techniques

Advanced PyTorch

Advanced JAX: Performance, Optimization, and Scale

Advanced Python Programming for Machine Learning

Advanced Adversarial Machine Learning

Advanced Bayesian Machine Learning

Advanced Federated Learning Techniques

Stage 4: Specialized AI Frontiers

Explore cutting-edge research in LLMs, advanced AI systems, and theoretical concepts.

Cutting-Edge LLM Research

LLM Compression and Acceleration Techniques

Agentic LLM Systems and Memory-Augmented Architectures

Advanced LLM Alignment: Constitutional AI and RLAIF

Mixture of Experts: Advanced Architecture, Training, and Scaling

Large Scale Distributed Retrieval-Augmented Generation

Advanced AI Systems & Theory

Meta-Learning and Few-Shot Adaptation in Foundation Models

Advanced Causal Inference in Machine Learning Systems

Advanced Compiler and Runtime Optimizations for ML Workloads

Advanced Quantum Machine Learning Algorithms and Implementation