Complete Generative AI Masterclass

Topics Covered

• What is Artificial Intelligence? History and evolution
• Types of AI: Narrow AI, General AI, Superintelligence
• Machine Learning vs Deep Learning vs Generative AI
• Generative AI revolution: GPT, DALL-E, Stable Diffusion
• AI career paths and opportunities in 2026 and beyond
• Why Python for AI? Python ecosystem overview
• Installing Python (3.10+) and setting up environment
• Development environments: Jupyter Notebook, VS Code, Google Colab
• Python basics: variables, data types, operators
• Control structures: if-else, loops (for, while)

• Functions: definition, parameters, return values
• Python data structures: lists, tuples, dictionaries, sets
• List comprehensions and dictionary comprehensions
• File handling: reading and writing files
• Error handling: try-except blocks
• Python modules and packages
• Virtual environments with venv/conda

Topics Covered

• Object-Oriented Programming in Python
• Classes and objects: __init__, self
• Inheritance and polymorphism
• Magic methods: __str__, __repr__, __len__
• Decorators and their usage
• Lambda functions and functional programming
• Map, filter, reduce functions
• Generators and iterators
• Context managers and 'with' statement
• Regular expressions for text processing

• Working with JSON and CSV data
• Introduction to NumPy: arrays and operations
• NumPy array operations, broadcasting, indexing
• Introduction to Pandas: DataFrames and Series
• Data loading, cleaning, and exploration with Pandas
• Data visualization basics with Matplotlib
• Plotting: line plots, bar charts, scatter plots, histograms

Topics Covered

• Why math matters for AI and ML
• Linear Algebra: vectors and vector operations
• Dot product and cross product
• Matrices and matrix operations
• Matrix multiplication and transposition
• Identity matrix and inverse matrix
• Eigenvalues and eigenvectors (intuition)
• Calculus: functions and limits
• Derivatives: concepts and rules
• Chain rule and partial derivatives

• Gradient: direction of steepest ascent
• Gradient descent algorithm basics
• Multivariable calculus for neural networks
• Probability theory: basics and terminology
• Probability distributions: uniform, normal (Gaussian)
• Expected value and variance
• Bayes' theorem and conditional probability

Topics Covered

• Statistics for data science
• Descriptive statistics: mean, median, mode
• Variance and standard deviation
• Correlation and covariance
• Hypothesis testing basics
• P-values and statistical significance
• Central Limit Theorem
• Confidence intervals
• Linear regression: mathematical foundation
• Cost functions and loss functions

• Optimization: local minima, global minima
• Convex vs non-convex functions
• Introduction to information theory
• Entropy and cross-entropy
• KL divergence
• Dimensionality reduction concepts
• Principal Component Analysis (PCA) intuition

Topics Covered

• What is Machine Learning? Formal definition
• Types of learning: Supervised, Unsupervised, Reinforcement
• Machine learning workflow: data, model, evaluation
• Training, validation, and test sets
• Overfitting and underfitting
• Bias-variance tradeoff
• Introduction to scikit-learn library
• Linear regression with scikit-learn
• Logistic regression for classification
• Decision trees: intuition and implementation

• Random forests ensemble method
• K-Nearest Neighbors (KNN) algorithm
• Support Vector Machines (SVM) basics
• Naive Bayes classifier
• Model evaluation metrics: accuracy, precision, recall, F1
• Confusion matrix and ROC curves
• Cross-validation techniques

Topics Covered

• From ML to Deep Learning: why neural networks?
• Perceptron: the building block
• Multi-layer perceptron (MLP) architecture
• Activation functions: sigmoid, tanh, ReLU, Leaky ReLU
• Forward propagation step-by-step
• Loss functions: MSE, cross-entropy
• Backpropagation algorithm intuition
• Gradient descent and its variants (SGD, Adam)
• Learning rate and hyperparameters
• Introduction to TensorFlow and Keras

• Building neural networks with Keras Sequential API
• Training and evaluating neural networks
• Regularization techniques: dropout, L1/L2
• Batch normalization
• Early stopping and model checkpoints
• Introduction to PyTorch basics
• PyTorch tensors and autograd

Topics Covered

• What is Generative AI? Definition and scope
• Discriminative vs Generative models
• Brief history: from GANs to GPT to Diffusion
• Modern generative AI landscape 2026
• Large Language Models (LLMs): GPT, Claude, Gemini
• Text-to-image models: DALL-E, Midjourney, Stable Diffusion
• Text-to-video: Sora, Runway, Pika
• Audio generation: music and speech
• Multimodal AI: combining text, image, audio
• AI model sizes and capabilities

• Prompt engineering: first introduction
• Using ChatGPT/Claude effectively
• AI ethics and responsible AI principles
• Bias in AI models and mitigation
• AI safety and alignment
• Future of generative AI
• Career opportunities in GenAI

Topics Covered

• Limitations of RNNs and LSTMs
• Attention mechanism: intuition and math
• Self-attention and multi-head attention
• Positional encoding in transformers
• Transformer architecture: encoder-decoder
• BERT: bidirectional encoder representations
• GPT architecture: decoder-only transformers
• Tokenization: BPE, WordPiece, SentencePiece
• Embedding layers and embedding spaces
• How GPT generates text: autoregressive generation

• Temperature, top-k, top-p sampling
• Model parameters: understanding 7B, 70B, 175B
• Context windows and attention span
• Fine-tuning vs prompt engineering vs RAG
• Pre-training and transfer learning
• Hugging Face Transformers library introduction
• Loading and using pre-trained models

Topics Covered

• OpenAI API overview and capabilities
• Getting API keys and authentication
• Current OpenAI chat models, and how to choose between them
• Chat completions API: messages and roles
• System, user, and assistant messages
• Completion parameters: temperature, max_tokens, top_p
• Streaming responses for real-time output
• Function calling with GPT-4
• JSON mode for structured outputs
• Vision API: GPT-4V for image understanding

• DALL-E API for image generation
• Whisper API for speech-to-text
• TTS (Text-to-Speech) API
• Embeddings API and semantic search
• Moderation API for content filtering
• Token counting and cost optimization
• Rate limiting and error handling

Topics Covered

• Prompt engineering as a discipline
• Anatomy of effective prompts
• Zero-shot, one-shot, few-shot learning
• Chain-of-Thought (CoT) prompting
• Tree of Thoughts (ToT) technique
• Self-consistency prompting
• Role prompting and persona design
• Instruction tuning concepts
• Prompt templates and variables
• Output formatting and constraints

• Handling hallucinations and reducing errors
• Prompt injection and safety
• Meta-prompting and prompt generation
• Multi-turn conversation design
• Context management in long conversations
• Prompt optimization and A/B testing
• Domain-specific prompting strategies

Topics Covered

• Anthropic Claude API and capabilities
• Claude's extended context window (100K+ tokens)
• Constitutional AI and Claude's safety features
• Google Gemini API and multimodal features
• Gemini Pro vs Gemini Ultra
• Meta Llama models and usage
• Open-source LLMs: Mistral, Falcon, MPT
• Running local LLMs with Ollama
• LLM comparison: GPT vs Claude vs Gemini
• Choosing the right model for your use case

• Cost comparison across providers
• Cohere API for enterprise solutions
• Azure OpenAI Service
• AWS Bedrock for LLM deployment
• Model routing and fallback strategies
• Multi-model applications
• API key management and security

Topics Covered

• What are embeddings? Semantic representations
• Word2Vec, GloVe, and modern embeddings
• OpenAI embeddings (text-embedding-ada-002)
• Sentence embeddings and document embeddings
• Cosine similarity and semantic search
• Vector databases: purpose and architecture
• Pinecone: managed vector database
• Weaviate: open-source vector DB
• Chroma: simple vector store
• Qdrant: performance-focused vector DB

• FAISS: Facebook AI similarity search
• Indexing strategies: flat, HNSW, IVF
• Metadata filtering in vector search
• Hybrid search: vector + keyword
• Building semantic search systems
• Recommendation systems with embeddings
• Clustering and visualization of embeddings

Topics Covered

• Introduction to LangChain framework
• LangChain architecture and components
• LLMs and Chat Models in LangChain
• Prompt templates and prompt engineering
• Output parsers: structured data from LLMs
• Chains: sequential processing
• LLMChain, SimpleSequentialChain, SequentialChain
• Memory types: ConversationBufferMemory, ConversationSummaryMemory
• Conversation chains with memory
• Document loaders: PDF, CSV, web pages

• Text splitters and chunking strategies
• Vector stores integration in LangChain
• Retrievers for information retrieval
• Combining LLMs with external data
• Question-answering over documents
• LangChain Expression Language (LCEL)
• Debugging and tracing in LangChain

Topics Covered

• Agents: autonomous task execution
• ReAct (Reasoning + Acting) agents
• Agent types: Zero-shot, Conversational
• Tools and toolkits in LangChain
• Creating custom tools for agents
• Google Search tool integration
• Python REPL tool for code execution
• Calculator and math tools
• API tools and web requests
• Database tools for SQL queries

• File system tools
• Multi-action agents
• Agent error handling and retries
• LangSmith for debugging and monitoring
• Callbacks and custom handlers
• Streaming responses in LangChain
• Cost tracking and optimization

Topics Covered

• What is RAG? Why RAG over fine-tuning?
• RAG architecture: retrieval + generation
• Building basic RAG pipeline
• Document preprocessing for RAG
• Chunking strategies: fixed-size, semantic
• Embedding generation for documents
• Vector store population
• Retrieval strategies: similarity search, MMR
• Reranking retrieved documents
• Context compression techniques

• Query transformation and expansion
• Retrieval quality evaluation
• Prompt engineering for RAG
• Handling long contexts
• Multi-query retrieval
• Hypothetical document embeddings (HyDE)
• Parent document retrieval

Topics Covered

• Advanced RAG architectures
• Self-RAG: self-reflective retrieval
• Corrective RAG (CRAG)
• Agentic RAG with LangGraph
• Multi-document RAG systems
• Hierarchical retrieval strategies
• Graph-based RAG
• Conversational RAG with memory
• RAG with structured data (tables, charts)
• Multimodal RAG: text + images

• RAG evaluation metrics: faithfulness, relevance
• RAGAS framework for evaluation
• RAG optimization techniques
• Caching strategies for RAG
• Production RAG deployment
• RAG monitoring and observability
• Security and privacy in RAG systems

Topics Covered

• LLM application architecture
• API integration best practices
• Prompt engineering mastery
• Vector database implementation
• RAG system design
• Agent orchestration

Topics Covered

• Introduction to computer vision
• Digital images: pixels, channels, color spaces
• Convolutional Neural Networks (CNNs)
• Convolution operation and filters
• Pooling layers: max pooling, average pooling
• CNN architectures: LeNet, AlexNet, VGG
• ResNet and skip connections
• Transfer learning with pre-trained models
• Image classification with CNNs
• Object detection: R-CNN, YOLO, SSD

• Image segmentation: semantic, instance
• OpenCV library for image processing
• PyTorch for computer vision
• torchvision and pre-trained models
• Data augmentation techniques
• Training CNNs: tips and tricks
• Model deployment for vision tasks

Topics Covered

• Introduction to generative models
• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• GAN training: generator and discriminator
• Common GAN variants: DCGAN, StyleGAN
• Diffusion models: DDPM, DDIM
• How Stable Diffusion works
• Latent diffusion models
• Text-to-image generation: Stable Diffusion
• Image-to-image transformation

• ControlNet for controlled generation
• LoRA: Low-Rank Adaptation
• DreamBooth for personalization
• Inpainting and outpainting
• Image upscaling with AI
• Automatic1111 and ComfyUI interfaces
• Hugging Face Diffusers library

Topics Covered

• Multimodal learning: combining modalities
• CLIP: connecting text and images
• CLIP embeddings and applications
• GPT-4V: vision understanding with GPT-4
• LLaVA: visual instruction tuning
• BLIP and BLIP-2 models
• Image captioning with transformers
• Visual question answering (VQA)
• Text-to-video generation: basics
• Video understanding with AI

• OCR with modern AI models
• Document understanding: LayoutLM
• Multimodal RAG systems
• Integrating vision and language models
• Building multimodal applications
• Evaluation of multimodal models
• Future of multimodal AI

Topics Covered

• Audio processing basics: waveforms, spectrograms
• Automatic Speech Recognition (ASR)
• Whisper model architecture and usage
• Speech-to-text applications
• Text-to-Speech (TTS) models
• Voice cloning with AI
• Bark, ElevenLabs, and other TTS systems
• Music generation with AI
• MusicGen, AudioLM, Jukebox
• Audio classification and tagging

• Voice activity detection
• Speaker diarization
• Audio enhancement and denoising
• Real-time audio processing
• Building voice assistants
• Multilingual speech models
• Audio-visual speech recognition

Topics Covered

• AI ethics fundamentals
• Bias in AI: sources and mitigation
• Fairness metrics and evaluation
• Transparency and explainability
• LIME and SHAP for model interpretation
• Privacy in AI: data protection
• Differential privacy
• Federated learning basics
• AI safety and alignment
• Constitutional AI principles

• Red teaming and adversarial testing
• Content moderation and filtering
• Deepfakes: detection and prevention
• AI regulations: GDPR, AI Act
• Responsible AI deployment
• Environmental impact of AI
• AI for social good

Topics Covered

• What is fine-tuning? When to fine-tune?
• Fine-tuning vs prompt engineering vs RAG
• Types of fine-tuning: full, parameter-efficient
• Transfer learning for LLMs
• Dataset preparation for fine-tuning
• Data annotation and labeling
• Instruction tuning datasets
• Supervised fine-tuning (SFT)
• Training loops and optimization
• Loss functions for language models

• Hyperparameter tuning
• Overfitting prevention in fine-tuning
• Evaluation metrics for fine-tuned models
• Benchmark datasets and tasks
• OpenAI fine-tuning API
• Fine-tuning current OpenAI models
• Cost considerations for fine-tuning

Topics Covered

• Parameter-Efficient Fine-Tuning (PEFT)
• LoRA: Low-Rank Adaptation explained
• LoRA implementation with PEFT library
• QLoRA: quantized LoRA for efficiency
• Adapter layers and adapter tuning
• Prefix tuning and prompt tuning
• P-tuning and P-tuning v2
• (IA)³: Infused Adapter by Inhibiting and Amplifying
• Choosing the right PEFT method
• 4-bit and 8-bit quantization

• bitsandbytes library usage
• Gradient checkpointing for memory
• Mixed precision training
• RLHF: Reinforcement Learning from Human Feedback
• DPO: Direct Preference Optimization
• Reward models and preference learning
• Alignment techniques overview

Topics Covered

• Open source LLM landscape
• Llama 2 and Llama 3 models
• Mistral and Mixtral models
• Falcon, MPT, and BLOOM
• Model cards and model documentation
• Downloading models from Hugging Face Hub
• Model quantization: GGUF, GPTQ, AWQ
• Running LLMs locally with Ollama
• llama.cpp for efficient inference
• vLLM for production serving

• Text Generation Inference (TGI)
• Model serving with FastAPI
• GPU vs CPU inference
• Batch inference optimization
• Caching and KV-cache optimization
• Model benchmarking: speed and quality
• Building custom inference servers

Topics Covered

• What are AI agents? Autonomous systems
• Agent types: simple reflex, goal-based, utility-based
• Perceive-Think-Act loop
• ReAct: reasoning and acting
• Plan-and-Execute agents
• Cognitive architectures for agents
• Agent memory systems
• Short-term vs long-term memory
• Semantic memory and episodic memory
• Working memory implementation

• Tool use and function calling
• Multi-step reasoning with agents
• Error handling and recovery
• Agent observability and logging
• LangGraph for agent workflows
• State machines for agents
• Human-in-the-loop agents

Topics Covered

• Multi-agent systems (MAS)
• Agent communication protocols
• Collaborative agents vs competitive agents
• Agent coordination and cooperation
• Hierarchical agent architectures
• AutoGPT and BabyAGI patterns
• Task decomposition and planning
• Sub-goal generation
• Agent self-evaluation and reflection
• MetaGPT and role-playing agents

• Specialized agent roles
• Agent orchestration frameworks
• Crew AI for multi-agent systems
• Agent security and sandboxing
• Rate limiting and cost control
• Agent performance optimization
• Debugging complex agent systems

Topics Covered

• What is MLOps? DevOps for ML
• ML lifecycle: develop, deploy, monitor
• Experiment tracking with MLflow
• Model versioning and registry
• Weights & Biases for experiment tracking
• Data versioning with DVC
• Model packaging and containerization
• Docker for ML applications
• Model serving patterns
• REST APIs for model serving

• Batch vs real-time inference
• Model deployment strategies
• Blue-green deployment for models
• Canary releases and A/B testing
• Model monitoring and observability
• Detecting model drift
• Retraining pipelines and triggers

Topics Covered

• Designing AI-powered applications
• Microservices architecture for AI
• FastAPI for ML/AI APIs
• Request validation and error handling
• Asynchronous processing with Celery
• Message queues: RabbitMQ, Redis
• Background tasks for AI workloads
• Caching strategies for AI apps
• Redis for response caching
• Database integration: PostgreSQL, MongoDB

• Storing embeddings in databases
• User authentication and authorization
• API rate limiting and throttling
• Webhook integrations
• Building SDKs for AI APIs
• API documentation with OpenAPI
• Postman and API testing

Topics Covered

• Enterprise prompt management
• Prompt versioning and tracking
• Prompt testing frameworks
• Evaluation metrics for prompts
• Automated prompt optimization
• Prompt tuning with PromptPerfect
• DSPy: programming with foundation models
• Prompt composition and chaining
• Dynamic prompt generation
• Context-aware prompting

• Multi-language prompt engineering
• Prompt security and injection prevention
• Guardrails for LLM outputs
• NeMo Guardrails framework
• Content filtering and moderation
• Prompt governance and compliance
• Cost optimization for prompts

Topics Covered

• Production vector database setup
• Pinecone at scale: indexes and namespaces
• Weaviate cluster configuration
• Qdrant for high-performance search
• Milvus for massive-scale vectors
• pgvector: Postgres extension
• Index optimization: HNSW tuning
• Quantization for vector storage
• Sharding and partitioning strategies
• Backup and disaster recovery

• Multi-tenancy in vector DBs
• Security and access control
• Monitoring vector database performance
• Cost optimization strategies
• Hybrid search at scale
• GraphRAG with knowledge graphs
• Neo4j integration with vectors

Topics Covered

• LLM observability challenges
• Tracing LLM calls end-to-end
• LangSmith for debugging
• OpenTelemetry for AI applications
• Logging best practices for LLMs
• Prompt and completion logging
• Cost tracking per request
• Token usage monitoring
• Latency optimization
• Error rate tracking

• User feedback collection
• Quality metrics for LLM outputs
• Automated quality evaluation
• Hallucination detection
• Safety and content filtering metrics
• Building observability dashboards
• Alerting and incident response

Topics Covered

• Query understanding and decomposition
• Query routing to different data sources
• Fusion retrieval: combining strategies
• Contextual compression and filtering
• Re-ranking models: Cohere, ColBERT
• Late interaction models
• Multi-hop reasoning in RAG
• Graph-based RAG architectures
• Temporal RAG for time-sensitive data
• Structured data RAG: SQL + vectors

• Text-to-SQL with LLMs
• RAG for code repositories
• Recursive retrieval strategies
• Adaptive retrieval: when to retrieve
• Active RAG with verification
• Multi-lingual RAG systems
• RAG for streaming data

Topics Covered

• LLM security vulnerabilities
• Prompt injection attacks and defenses
• Jailbreaking prevention
• Data leakage and privacy concerns
• PII detection and redaction
• Model inversion attacks
• Adversarial attacks on AI
• Robustness testing for LLMs
• Red teaming AI systems
• Safety classifiers and filters

• Constitutional AI implementation
• Alignment faking detection
• Secure API design for AI
• Authentication and authorization
• Secrets management for API keys
• Compliance: GDPR, HIPAA, SOC2
• Building audit trails

Topics Covered

• Cloud platforms for AI: AWS, GCP, Azure
• AWS SageMaker for ML/AI
• AWS Bedrock for LLM deployment
• Azure OpenAI Service
• Google Cloud Vertex AI
• GPU instances: A100, H100, V100
• Kubernetes for AI workloads
• Ray for distributed computing
• Serverless AI with AWS Lambda
• Cloud Run for containerized AI

• Auto-scaling AI services
• Load balancing for AI APIs
• CDN for AI-generated content
• Edge AI deployment
• Multi-cloud strategies
• Cost optimization: spot instances, reserved
• Infrastructure as Code: Terraform

Topics Covered

• Building AI products: strategy
• Product-market fit for AI
• User experience design for AI
• Conversational UI best practices
• AI feature discovery and iteration
• A/B testing AI features
• Product analytics for AI
• User feedback loops
• MVP development for AI products
• Go-to-market for AI products

• Pricing strategies for AI
• Monetization: API-first, SaaS, usage-based
• Building AI product roadmaps
• Competitive analysis in AI space
• AI product metrics and KPIs
• Customer success for AI products
• Building AI product teams

Topics Covered

• End-to-end AI system design
• Production deployment
• Monitoring and observability
• Security implementation
• Scalability optimization
• Cost management

Topics Covered

• LLM training pipeline overview
• Data collection and curation
• Data cleaning and filtering
• Tokenizer training: BPE, SentencePiece
• Model architecture design
• Choosing model size and layers
• Attention mechanisms variants
• Position embeddings: absolute, relative, RoPE
• Activation functions for transformers
• Initialization strategies

• Parallelism: data, model, pipeline
• Distributed training frameworks
• Gradient accumulation
• Mixed precision training
• Optimizer choice: Adam, AdamW, Lion
• Learning rate schedules
• Pre-training objectives

Topics Covered

• Setting up training environment
• PyTorch Lightning for training
• Hugging Face Accelerate library
• DeepSpeed for large models
• FSDP: Fully Sharded Data Parallel
• Flash Attention for efficiency
• Checkpointing and recovery
• Evaluation during training
• Perplexity and other metrics
• Early stopping strategies

• Debugging training issues
• OOM errors and solutions
• Gradient clipping and stabilization
• Post-training: alignment and safety
• Instruction tuning pipeline
• Dataset quality for alignment
• Evaluation of trained models

Topics Covered

• Advanced NLP tasks beyond generation
• Named Entity Recognition (NER) at scale
• Relation extraction from text
• Coreference resolution
• Semantic role labeling
• Dependency parsing
• Information extraction pipelines
• Document understanding
• Table extraction and understanding
• Knowledge graph construction from text

• Text classification: hierarchical, multi-label
• Sentiment analysis: aspect-based
• Topic modeling with LLMs
• Text clustering and categorization
• Zero-shot and few-shot classification
• Cross-lingual NLP
• Low-resource language processing

Topics Covered

• Code generation with LLMs
• Code completion models
• Program synthesis
• Code translation between languages
• Bug detection and fixing
• Code review automation
• Test case generation
• Documentation generation
• Code search and retrieval
• Execution-based code evaluation

• Mathematical reasoning with LLMs
• Symbolic reasoning integration
• Tool use for complex reasoning
• Multi-step problem solving
• Verification and validation
• Chain-of-thought for code
• Self-debugging agents

Topics Covered

• Reinforcement Learning basics review
• RL for language models
• Reward modeling for RLHF
• Preference datasets and collection
• Proximal Policy Optimization (PPO)
• PPO for language models
• Reward hacking and mitigation
• KL divergence constraint
• Online vs offline RL
• Constitutional AI with RL

• Multi-objective RLHF
• DPO: Direct Preference Optimization
• IPO and other alignment methods
• Reward model training
• Evaluation of aligned models
• Scaling RLHF
• Future of alignment research

Topics Covered

• Reading research papers effectively
• Understanding paper structure
• Key AI conferences: NeurIPS, ICML, ICLR
• ArXiv and Hugging Face Papers
• State-of-the-art tracking
• Implementing papers from scratch
• Reproducing research results
• Ablation studies and experiments
• Writing technical blog posts
• Contributing to research discussions

• Benchmarking new techniques
• Open source contributions to AI
• Creating research reproducibility
• Building on existing research
• Novel applications of research
• Research ethics and attribution
• Staying current with AI advances

Topics Covered

• Mixture of Experts (MoE) models
• Sparse models and efficiency
• Long context models: 100K+ tokens
• Efficient attention mechanisms
• Quantization: GPTQ, AWQ, GGUF
• Speculative decoding
• Model merging and mixing
• Continuous learning and lifelong learning
• Few-shot in-context learning
• Meta-learning for AI

• Retrieval-augmented pre-training
• Multimodal fusion techniques
• Chain-of-thought prompting variants
• Tool-augmented LLMs
• Neuro-symbolic AI
• AI for science: protein folding, drug discovery
• Frontier capabilities and risks

Topics Covered

• AI startup landscape 2026 and beyond
• Identifying AI opportunities
• Market research for AI products
• Building vs buying foundation models
• Vertical AI applications
• AI-native product design
• Technical co-founder skills
• Building MVP for AI products
• Fundraising for AI startups
• Pitch deck for AI companies

• Go-to-market strategies
• Distribution channels for AI
• Pricing and business models
• Unit economics for AI
• Building AI teams
• Hiring AI talent
• AI startup case studies

Topics Covered

• AI consulting services landscape
• Building consulting practice
• Client discovery and scoping
• AI project estimation
• Proposal writing for AI projects
• Contract negotiation
• AI audit and assessment
• Strategy consulting for AI
• Implementation services
• Training and enablement

• Ongoing support models
• Pricing consulting services
• Building case studies
• Marketing AI services
• Networking and partnerships
• Scaling consulting business
• Freelance platforms for AI

Topics Covered

• AI career paths: engineer, researcher, scientist
• Resume optimization for AI roles
• Portfolio building: GitHub, blog, projects
• LinkedIn for AI professionals
• Networking in AI community
• Job search strategies
• Company research: startups vs big tech
• Application process optimization
• AI interview preparation
• Technical interview: coding + ML

• System design for AI systems
• Behavioral interviews
• Take-home assignments
• Salary negotiation for AI roles
• Offer evaluation
• Continuous learning plans
• Building personal brand

Topics Covered

• Specialization paths overview
• Path 1: LLM Engineer (production systems, scaling)
• Path 2: AI Research Engineer (paper implementation, novel techniques)
• Path 3: Applied AI Engineer (domain-specific applications)
• Path 4: MLOps/AI Infrastructure Engineer (deployment, monitoring)
• Path 5: AI Product Manager (strategy, roadmap, execution)
• Path 6: Prompt Engineer (advanced prompting, optimization)
• Path 7: AI Ethics/Safety Researcher (alignment, safety)
• Path 8: AI Consultant (client services, implementation)
• Building expertise in chosen path

• Advanced projects for specialization
• Networking in specialized field
• Thought leadership
• Contributing to community
• Teaching and mentoring
• Continuous specialization learning

Topics Covered

• AI open source ecosystem
• Contributing to major projects
• Hugging Face contributions
• LangChain contributions
• PyTorch and TensorFlow contributions
• Creating open source AI tools
• Documentation contributions
• Building community around projects
• Speaking at meetups and conferences
• Writing influential blog posts

• YouTube and content creation
• Twitter/X for AI professionals
• Discord and community management
• Mentoring others in AI
• Teaching AI online
• Building reputation
• Long-term community involvement

Topics Covered

• Current state of AI (2026)
• AGI timeline and implications
• Multimodal AI evolution
• AI agents and automation
• Specialized AI systems
• AI in every industry
• Regulatory landscape evolution
• AI safety and alignment future
• Economic impact of AI
• Job market evolution

• Skills for the AI future
• Continuous adaptation strategies
• Ethical considerations ahead
• Your unique value proposition
• Building resilient AI career
• Lifelong learning plans
• Contributing to AI's future

Topics Covered

• AI interview process deep dive
• Company-specific preparation: OpenAI, Anthropic, Google
• Startup interviews vs big tech
• Technical rounds: ML fundamentals
• LLM-specific questions
• Coding interviews for AI roles
• System design for AI systems
• ML system design interviews
• Designing AI products in interviews
• Behavioral interviews: STAR method

• Portfolio presentation
• Take-home projects excellence
• Negotiation strategies
• Evaluating offers
• Onboarding preparation
• First 90 days plan
• Career launch checklist

Topics Covered

• Grand finale project planning
• Innovative AI application idea
• Complete system architecture
• Technology stack selection
• Development workflow
• Agile sprint planning
• Team collaboration (if applicable)
• Code organization and structure
• Documentation strategy
• Testing strategy

Topics Covered

• Implementation completion
• Advanced features integration
• Performance optimization
• Security hardening
• Comprehensive testing
• User experience refinement
• Production deployment
• Monitoring setup
• Documentation writing
• Demo video creation
• Presentation preparation
• Open source release

Topics Covered

• Professional presence establishment
• Portfolio website showcase
• GitHub profile optimization
• LinkedIn thought leadership
• Technical blog authority
• Conference speaking applications
• Community leadership
• Mentorship opportunities
• Teaching and training
• Consulting setup

• Startup plans (if applicable)
• Job offers evaluation
• Career trajectory planning
• Network expansion
• Continuous innovation
• Giving back to community
• Long-term AI career vision