1
Advanced Python for AI
OOP & design patterns, functional programming, generators & iterators, async, type hinting, memory management, profiling, packaging & dependencies.
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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
Data Structures & Algorithms for AI Engineers
Arrays, strings, linked lists, stacks, queues, trees, graphs, hashing, recursion, backtracking, sorting, searching, time & space complexity.
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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
Software Engineering for AI
Clean code, SOLID, API design, unit/integration testing, logging & monitoring, Git advanced, CI/CD pipelines.
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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
Linear Algebra
Vector spaces, matrix operations, eigenvalues & eigenvectors, SVD, norms, linear transformations.
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Learning objectives:
• Work with vector spaces and matrix operations
• Apply eigenvalues, eigenvectors, SVD
• Use norms and linear transformations
5
Calculus & Optimization
Derivatives, partial derivatives, chain rule, gradients, Jacobian & Hessian, optimization algorithms.
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Learning objectives:
• Apply derivatives, partial derivatives, chain rule
• Use gradients, Jacobian, Hessian
• Apply optimization algorithms
6
Probability & Statistics
Random variables, distributions, Bayesian inference, maximum likelihood, hypothesis testing, A/B testing, statistical significance.
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Learning objectives:
• Work with random variables and distributions
• Apply Bayesian inference and maximum likelihood
• Use hypothesis testing, A/B testing, statistical significance
7
ML Fundamentals
ML pipeline, bias-variance, overfitting & regularization, evaluation metrics, feature engineering.
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Learning objectives:
• Build ML pipeline and understand bias-variance
• Address overfitting and regularization
• Use evaluation metrics and feature engineering
8
Supervised Learning
Linear models, tree-based models, ensemble methods, gradient boosting, model interpretation.
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Learning objectives:
• Apply linear and tree-based models
• Use ensemble methods and gradient boosting
• Interpret models
9
Unsupervised Learning
Clustering, dimensionality reduction, anomaly detection, topic modeling.
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Learning objectives:
• Apply clustering and dimensionality reduction
• Detect anomalies and perform topic modeling
10
Advanced ML
Imbalanced data, multi-label classification, time series forecasting, recommender systems, graph ML.
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Learning objectives:
• Handle imbalanced data and multi-label classification
• Build time series and recommender systems
• Apply graph ML
11
Neural Networks
Perceptron, activation functions, backpropagation, optimization strategies, regularization.
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Learning objectives:
• Understand perceptron and activation functions
• Apply backpropagation and optimization strategies
• Use regularization
12
CNN & Computer Vision
CNN architecture, object detection, segmentation, transfer learning, vision transformers.
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Learning objectives:
• Design CNN and perform object detection, segmentation
• Apply transfer learning and vision transformers
13
NLP & Sequence Models
Text preprocessing, RNN, LSTM, GRU, attention, Transformers.
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Learning objectives:
• Preprocess text and use RNN, LSTM, GRU
• Apply attention and Transformers
14
Multimodal AI
Vision + text, audio + text, image captioning, CLIP models.
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Learning objectives:
• Build vision+text and audio+text models
• Apply image captioning and CLIP
15
Transformer Architecture Deep Dive
Self attention, multi-head attention, encoder-decoder, scaling laws.
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Learning objectives:
• Understand self attention and multi-head attention
• Apply encoder-decoder and scaling laws
16
Large Language Models
GPT architecture, pretraining, fine-tuning, instruction tuning, LoRA & PEFT.
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Learning objectives:
• Understand GPT and pretraining
• Apply fine-tuning, instruction tuning, LoRA & PEFT
17
Prompt Engineering
Prompt patterns, few-shot prompting, chain of thought, system prompts, guardrails.
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Learning objectives:
• Apply prompt patterns and few-shot prompting
• Use chain of thought and system prompts
• Implement guardrails
18
Retrieval Augmented Generation (RAG)
Embeddings, vector databases, chunking strategies, hybrid search, reranking.
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Learning objectives:
• Use embeddings and vector databases
• Apply chunking, hybrid search, reranking
19
AI Agents & Tool Calling
Agent architectures, tool integration, function calling, multi-agent systems, workflow automation.
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Learning objectives:
• Design agent architectures and integrate tools
• Use function calling and multi-agent systems
• Automate workflows
20
Model Deployment
FastAPI, gRPC, Docker, Kubernetes, model versioning.
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Learning objectives:
• Deploy with FastAPI, gRPC, Docker, Kubernetes
• Apply model versioning
21
MLOps
MLflow, DVC, feature stores, monitoring, drift detection.
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Learning objectives:
• Use MLflow, DVC, feature stores
• Apply monitoring and drift detection
22
Distributed AI Systems
Distributed training, data parallelism, model parallelism, GPU optimization, inference scaling.
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Learning objectives:
• Apply distributed training and data/model parallelism
• Optimize GPU and scale inference
23
Cloud AI Engineering
AWS, GCP, Azure, serverless AI, auto-scaling.
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Learning objectives:
• Use AWS, GCP, Azure for AI
• Apply serverless AI and auto-scaling
24
Responsible AI
Bias & fairness, explainability, ethical AI, governance.
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Learning objectives:
• Address bias, fairness, explainability
• Apply ethical AI and governance
25
AI Security
Prompt injection, model attacks, data poisoning, secure deployment.
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Learning objectives:
• Defend against prompt injection and model attacks
• Address data poisoning and secure deployment
26
Reinforcement Learning
MDP, Q-Learning, policy gradient, deep RL.
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Learning objectives:
• Model with MDP and apply Q-Learning
• Use policy gradient and deep RL
27
Edge AI
Model quantization, ONNX, TensorRT, mobile AI.
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Learning objectives:
• Apply quantization, ONNX, TensorRT
• Deploy mobile AI
28
AI System Design
End-to-end architecture, real-time AI, feature stores, high availability.
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Learning objectives:
• Design end-to-end and real-time AI systems
• Use feature stores and high availability
29
AI Research Foundations
Reading papers, implementing papers, experiment design, benchmarking.
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Learning objectives:
• Read and implement research papers
• Design experiments and run benchmarks
30
End-to-End ML System
Data → Model → Deployment → Monitoring.
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Learning objectives:
• Build full pipeline from data to deployment and monitoring
31
LLM Production App
RAG chatbot, authentication, monitoring, scaling.
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Learning objectives:
• Build RAG chatbot with authentication, monitoring, scaling
32
AI SaaS Architecture
Multi-tenant AI, billing integration, logging & observability.
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Learning objectives:
• Design multi-tenant AI with billing and observability