Tech Training • Professional Certification

AI Stack Training

Duration 80 Days
Mode of Training Online
Level Advanced

Overview – AI Stack Course Online

The AI Stack Course Online equips learners with comprehensive knowledge of Artificial Intelligence (AI) technologies and modern tools, enabling them to design, develop, and deploy intelligent solutions. This program provides a practical, hands-on learning experience. Cygnisoft’s AI Stack Online Training is designed to meet this demand, giving learners practical knowledge of the tools, frameworks, and workflows used in real projects.

The program comes with an updated curriculum focused on the latest features in AI Stack development, ensuring you acquire skills that are relevant today. Learners from anywhere in the world can access this course through flexible online learning options tailored to career growth.

What You Will Learn in AI Stack Online Training

This program covers both foundational and advanced AI concepts, designed to provide real-world exposure. Key highlights include:

  • Core AI and machine learning workflows
  • Deep learning frameworks and deployment methods
  • Cloud integration with AI Stack platforms
  • Advanced automation and data engineering techniques
  • Hands-on labs with updated features

The AI Stack Training follows industry best practices, enabling learners to become job-ready. By the end of the course, you will be confident in handling AI Stack projects from start to finish.

Why Choose Cygnisoft for AI Stack Online Training?

Cygnisoft is a leading provider of IT and AI training with a focus on practical, future-ready skills. Our AI Stack Online Training is built to help learners acquire real-world expertise and prepare for careers in AI development and implementation.

Key benefits of choosing Cygnisoft:

  • Updated Curriculum: Stay current with the latest AI Stack features and industry trends.
  • Expert Trainers: Learn directly from industry professionals with years of practical experience.
  • Hands-On Learning: Work on real-time projects for practical exposure.
  • Flexible Learning Modes: Fully online training to fit your schedule.
  • Career Support: Guidance for career development and job readiness.

When you enroll in Cygnisoft’s AI Stack Online Training, you’re not just learning theory—you’re preparing for a career in AI that is aligned with industry requirements.

Enroll in AI Stack Online Training

AI professionals are in high demand, and now is the right time to upgrade your skills. With the AI Stack Course Online, you gain knowledge aligned with the latest industry standards.

Cygnisoft ensures you learn faster, smarter, and more effectively with a hands-on, practical approach. Take the first step toward a future-ready AI career today!

You may also see course Curriculum

AI Stack Course Overview

  • Introduction, Q&A, setting expectations
  • Overview of Generative AI, APIs, software installation
  • Python basics (variables, operators, input/output)
  • Control flow – For/While loops
  • Python data structures – Lists, Tuples, Dictionaries, Sets
  • Python functions – definition, scope, recursion
  • Python OOPs – Classes, objects, inheritance, polymorphism
  • Advanced Python – decorators, iterators, generators
  • Exception handling, shallow vs deep copy
  • Pandas for data manipulation
  • Introduction to NLP – preprocessing (tokenization, stopwords)
  • NLP embeddings (Word2Vec, TF-IDF, sentence embeddings)
  • Word2Vec in ML – CBOW, Skip-gram (theory)
  • Word2Vec practical implementation
  • History of LLMs, RNN → LSTM → GRU evolution
  • Encoder-Decoder architecture for Seq2Seq tasks
  • Attention mechanism – why it matters
  • Transformer architecture – Part 1 (embeddings + positional encoding)
  • Transformer architecture – Part 2 (multi-head attention, residuals)
  • Transformer architecture – Part 3 (feedforward, training pipeline)
  • Introduction to OpenAI & LLMs
  • Decoder models – GPT family (how they work)
  • Encoder-Decoder models – T5
  • Encoder-only models – BERT
  • Quantization techniques – GGML vs GGUF
  • Hugging Face API integration with LangChain
  • LangChain memory – buffer, summary, window
  • LangChain chains (LLMChain)
  • LangChain runnables
  • Structured output, output parsers in LangChain
  • Document loaders in LangChain (PDF, TXT, APIs)
  • Text splitting – recursive & character splitters
  • Vector databases overview (FAISS, Pinecone, Chroma)
  • Hands-on with FAISS for similarity search
  • What is RAG? Architecture & workflow
  • RAG application – Retriever
  • RAG with LangSmith (observability & testing)
  • Naïve RAG application
  • Advanced RAG pipelines (multi-query, rerankers)
  • RAG with YouTube transcripts (YouTube chat system)
  • Evaluation metrics for LLMs – ROUGE, BLEU, METEOR, CIDEr
  • Ollama – lightweight LLM platform
  • Tokens & parameters in LLMs (temperature, top-p, context length)
  • LangChain prompts – templates & formats
  • Prompt engineering Part 1 – zero/few-shot, CoT prompts
  • Prompt engineering Part 2 – advanced templates & role prompting
  • Agents – intro to tools in LangChain
  • Agent tool calling – structured API calls
  • Building a Weather API agent (hardcoded + API)
  • LangChain ReAct agent (Reasoning + Acting)
  • Multi-agents with yFinance use case
  • Crew AI – multi-agent orchestration framework
  • LangChain vs LangGraph – agent comparison
  • Sequential workflow in LangGraph
  • Parallel workflow in LangGraph
  • Iterative workflow in LangGraph
  • Tools in LangGraph (search, calculators, APIs)
  • Building a chatbot in LangGraph
  • Chatbot + Tools integration with LangGraph
  • Threaded, streaming chatbot with history
  • SQLite + MemorySaver integration for chatbots
  • LangSmith integration with LangGraph chatbots
  • RAG + Agentic applications using LangFlow (no-code)
  • Multi-agent orchestration using OpenAI Swarm
  • Guardrails overview & implementation for safe responses
  • Building an ATS (Applicant Tracking System) with Google Gemini Pro
  • Generate test cases & artifacts using GenAI
  • AI DevOps – GitHub Actions for CI/CD pipelines
  • LLMOps with LangSmith – Part 1 (tracing)
  • LLMOps with LangSmith – Part 2 (evaluation)
  • LLMOps with LangSmith – Part 3 (dataset management)
  • LLMOps with LangSmith – Part 4 (experimentation)
  • Multi-query PDF application with Google Gemini Pro
  • Deploying GenAI apps on Streamlit Cloud
  • MCP – Session 1 (overview + concepts)
  • MCP – Session 2 (building MCP pipelines)
  • MCP – Session 3 (MCP + agent integration)
  • Docker installation & basics
  • Docker Part 1 – Containers, images, hands-on
  • Docker Part 2 – Insurance Premium Prediction Project + Git basics (repos, commits, history check)

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