Foundation Skills
These are
the core concepts every AI/ML learner should master before moving into advanced
Gen AI topics.
AI & ML Basics
Learn the
fundamentals of machine learning, model training, supervised learning,
unsupervised learning, and neural networks.
Important
Tools & Libraries:
- Scikit-learn
- TensorFlow
- PyTorch
- Keras
Python Programming
Python is
the primary programming language for AI and Gen AI development.
Key
Topics:
- Python syntax
- Functions and classes
- APIs
- File handling
- Data structures
Important
Tools:
- Python
- Jupyter Notebooks
- VS Code
- Anaconda
Math & Data
Strong
mathematical understanding improves model building and data analysis.
Core
Areas:
- Linear algebra
- Statistics
- Probability
- Data analysis
Important
Libraries:
- NumPy
- Pandas
- SciPy
- Matplotlib
Core Techniques
These
skills are directly connected to building powerful AI applications.
Prompt Engineering
Prompt
engineering helps generate better outputs from large language models.
Learn
About:
- Prompt design
- Few-shot prompting
- Chain-of-thought prompting
- Context optimization
Popular
Tools:
- FlowGPT
- Guidance
- DSPy
- PromptPerfect
RAG (Retrieval-Augmented Generation)
RAG
combines external knowledge retrieval with LLMs to improve accuracy.
Key
Concepts:
- Vector databases
- Embeddings
- Semantic search
- Document retrieval
Popular
Tools:
- Pinecone
- ChromaDB
- FAISS
- Weaviate
Fine-Tuning
Fine-tuning
customizes pre-trained models for specific tasks and domains.
Key
Techniques:
- LoRA
- QLoRA
- PEFT
- OpenAI Fine-Tuning
Evaluation & Guardrails
Evaluation
ensures AI systems remain safe, reliable, and accurate.
Important
Areas:
- Hallucination detection
- Bias control
- Safety testing
- AI governance
Popular
Tools:
- Guardrails AI
- TruLens
- DeepChecks
- Llama Guard
Generative Models
These
technologies power modern AI systems that create text, images, audio, and
multimodal outputs.
LLMs (Large Language Models)
LLMs are
the foundation of modern conversational AI systems.
Examples:
- OpenAI GPT
- Claude
- LLaMA
- Mistral
Image Generation
Image
models create visual content from text prompts.
Popular
Platforms:
- Midjourney
- DALL·E
- Adobe Firefly
- Stable Diffusion
Audio & Video AI
These
tools generate or edit audio and video content.
Popular
Tools:
- Runway
- Descript
- Kaiber
- Synthesia
Multimodal Models
Multimodal
AI can understand text, images, audio, and video together.
Examples:
- GPT-4o
- Gemini
- LLaVA
- CLIP
Agentic Capabilities
These
skills focus on autonomous AI systems and workflow automation.
AI Agents
AI agents
can plan tasks, use tools, and automate complex workflows.
Popular
Frameworks:
- AutoGPT
- BabyAGI
- CrewAI
- Open Agents
Workflow Orchestration
Workflow
tools connect AI systems with automation platforms.
Popular
Platforms:
- Make.com
- n8n
- Zapier
- Prefect
Advanced Growth
These are
advanced topics for scaling AI systems professionally.
Deployment & Scaling
Learn how
to deploy AI systems into production environments.
Important
Platforms:
- Docker
- Google Cloud Vertex AI
- AWS Bedrock
- Azure OpenAI
Specialization
Choose an
industry domain and build AI expertise in that field.
Examples:
- Enterprise Automation
- FinTech AI
- LegalTech AI
- Healthcare AI
MLOps & Observability
MLOps
helps monitor, manage, and improve AI systems in production.
Important
Tools:
- MLflow
- Weights & Biases
- LangSmith
- Datadog
Learning Path
Beginner Stage
- Learn Python
- Study AI/ML basics
- Practice data analysis with
NumPy and Pandas
- Understand machine learning
fundamentals
Intermediate Stage
- Learn prompt engineering
- Build RAG applications
- Experiment with LLM APIs
- Explore fine-tuning methods
Advanced Stage
- Build AI agents
- Learn workflow automation
- Deploy applications to cloud
platforms
- Study MLOps and monitoring
