AI Engineer Master Program

1 Locked

Advanced Python for AI

OOP & design patterns, functional programming, generators & iterators, async, type hinting, memory management, profiling, packaging & dependencies.

Please enroll in this course to access the modules.

Learning objectives Apply OOP and design patterns Use functional programming, generators, iterators Apply async programming and type hinting Manage memory and optimize with profiling Handle packaging and dependency management

2 Locked

Data Structures & Algorithms for AI Engineers

Arrays, strings, linked lists, stacks, queues, trees, graphs, hashing, recursion, backtracking, sorting, searching, time & space complexity.

Please enroll in this course to access the modules.

Learning objectives Implement arrays, strings, linked lists, stacks, queues Work with trees and graphs Apply hashing, recursion, backtracking Use sorting and searching Analyze time and space complexity

3 Locked

Software Engineering for AI

Clean code, SOLID, API design, unit/integration testing, logging & monitoring, Git advanced, CI/CD pipelines.

Please enroll in this course to access the modules.

Learning objectives Apply clean code and SOLID principles Design APIs and write unit/integration tests Use logging and monitoring Apply Git advanced and CI/CD pipelines

4 Locked

Linear Algebra

Vector spaces, matrix operations, eigenvalues & eigenvectors, SVD, norms, linear transformations.

Please enroll in this course to access the modules.

Learning objectives Work with vector spaces and matrix operations Apply eigenvalues, eigenvectors, SVD Use norms and linear transformations

5 Locked

Calculus & Optimization

Derivatives, partial derivatives, chain rule, gradients, Jacobian & Hessian, optimization algorithms.

Please enroll in this course to access the modules.

Learning objectives Apply derivatives, partial derivatives, chain rule Use gradients, Jacobian, Hessian Apply optimization algorithms

6 Locked

Probability & Statistics

Random variables, distributions, Bayesian inference, maximum likelihood, hypothesis testing, A/B testing, statistical significance.

Please enroll in this course to access the modules.

Learning objectives Work with random variables and distributions Apply Bayesian inference and maximum likelihood Use hypothesis testing, A/B testing, statistical significance

7 Locked

ML Fundamentals

ML pipeline, bias-variance, overfitting & regularization, evaluation metrics, feature engineering.

Please enroll in this course to access the modules.

Learning objectives Build ML pipeline and understand bias-variance Address overfitting and regularization Use evaluation metrics and feature engineering

8 Locked

Supervised Learning

Linear models, tree-based models, ensemble methods, gradient boosting, model interpretation.

Please enroll in this course to access the modules.

Learning objectives Apply linear and tree-based models Use ensemble methods and gradient boosting Interpret models

9 Locked

Unsupervised Learning

Clustering, dimensionality reduction, anomaly detection, topic modeling.

Please enroll in this course to access the modules.

Learning objectives Apply clustering and dimensionality reduction Detect anomalies and perform topic modeling

10 Locked

Advanced ML

Imbalanced data, multi-label classification, time series forecasting, recommender systems, graph ML.

Please enroll in this course to access the modules.

Learning objectives Handle imbalanced data and multi-label classification Build time series and recommender systems Apply graph ML

11 Locked

Neural Networks

Perceptron, activation functions, backpropagation, optimization strategies, regularization.

Please enroll in this course to access the modules.

Learning objectives Understand perceptron and activation functions Apply backpropagation and optimization strategies Use regularization

12 Locked

CNN & Computer Vision

CNN architecture, object detection, segmentation, transfer learning, vision transformers.

Please enroll in this course to access the modules.

Learning objectives Design CNN and perform object detection, segmentation Apply transfer learning and vision transformers

13 Locked

NLP & Sequence Models

Text preprocessing, RNN, LSTM, GRU, attention, Transformers.

Please enroll in this course to access the modules.

Learning objectives Preprocess text and use RNN, LSTM, GRU Apply attention and Transformers

14 Locked

Multimodal AI

Vision + text, audio + text, image captioning, CLIP models.

Please enroll in this course to access the modules.

Learning objectives Build vision+text and audio+text models Apply image captioning and CLIP

15 Locked

Transformer Architecture Deep Dive

Self attention, multi-head attention, encoder-decoder, scaling laws.

Please enroll in this course to access the modules.

Learning objectives Understand self attention and multi-head attention Apply encoder-decoder and scaling laws

16 Locked

Large Language Models

GPT architecture, pretraining, fine-tuning, instruction tuning, LoRA & PEFT.

Please enroll in this course to access the modules.

Learning objectives Understand GPT and pretraining Apply fine-tuning, instruction tuning, LoRA & PEFT

17 Locked

Prompt Engineering

Prompt patterns, few-shot prompting, chain of thought, system prompts, guardrails.

Please enroll in this course to access the modules.

Learning objectives Apply prompt patterns and few-shot prompting Use chain of thought and system prompts Implement guardrails

18 Locked

Retrieval Augmented Generation (RAG)

Embeddings, vector databases, chunking strategies, hybrid search, reranking.

Please enroll in this course to access the modules.

Learning objectives Use embeddings and vector databases Apply chunking, hybrid search, reranking

19 Locked

AI Agents & Tool Calling

Agent architectures, tool integration, function calling, multi-agent systems, workflow automation.

Please enroll in this course to access the modules.

Learning objectives Design agent architectures and integrate tools Use function calling and multi-agent systems Automate workflows

20 Locked

Model Deployment

FastAPI, gRPC, Docker, Kubernetes, model versioning.

Please enroll in this course to access the modules.

Learning objectives Deploy with FastAPI, gRPC, Docker, Kubernetes Apply model versioning

21 Locked

MLOps

MLflow, DVC, feature stores, monitoring, drift detection.

Please enroll in this course to access the modules.

Learning objectives Use MLflow, DVC, feature stores Apply monitoring and drift detection

22 Locked

Distributed AI Systems

Distributed training, data parallelism, model parallelism, GPU optimization, inference scaling.

Please enroll in this course to access the modules.

Learning objectives Apply distributed training and data/model parallelism Optimize GPU and scale inference

23 Locked

Cloud AI Engineering

AWS, GCP, Azure, serverless AI, auto-scaling.

Please enroll in this course to access the modules.

Learning objectives Use AWS, GCP, Azure for AI Apply serverless AI and auto-scaling

24 Locked

Responsible AI

Bias & fairness, explainability, ethical AI, governance.

Please enroll in this course to access the modules.

Learning objectives Address bias, fairness, explainability Apply ethical AI and governance

25 Locked

AI Security

Prompt injection, model attacks, data poisoning, secure deployment.

Please enroll in this course to access the modules.

Learning objectives Defend against prompt injection and model attacks Address data poisoning and secure deployment

26 Locked

Reinforcement Learning

MDP, Q-Learning, policy gradient, deep RL.

Please enroll in this course to access the modules.

Learning objectives Model with MDP and apply Q-Learning Use policy gradient and deep RL

27 Locked

Edge AI

Model quantization, ONNX, TensorRT, mobile AI.

Please enroll in this course to access the modules.

Learning objectives Apply quantization, ONNX, TensorRT Deploy mobile AI

28 Locked

AI System Design

End-to-end architecture, real-time AI, feature stores, high availability.

Please enroll in this course to access the modules.

Learning objectives Design end-to-end and real-time AI systems Use feature stores and high availability

29 Locked

AI Research Foundations

Reading papers, implementing papers, experiment design, benchmarking.

Please enroll in this course to access the modules.

Learning objectives Read and implement research papers Design experiments and run benchmarks

30 Locked

End-to-End ML System

Data → Model → Deployment → Monitoring.

Please enroll in this course to access the modules.

Learning objectives Build full pipeline from data to deployment and monitoring

31 Locked

LLM Production App

RAG chatbot, authentication, monitoring, scaling.

Please enroll in this course to access the modules.

Learning objectives Build RAG chatbot with authentication, monitoring, scaling

32 Locked

AI SaaS Architecture

Multi-tenant AI, billing integration, logging & observability.

Please enroll in this course to access the modules.

Learning objectives Design multi-tenant AI with billing and observability