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GenAI ML Training

GenAI ML Course Training in Hyderabad

GenAI ML is an open source advanced client side Javascript framework based on MVC. The GenAI ML course from php2ranjan covers both the basic and advanced concepts to create SPA starting from designing to coding and testing as a complete cycle. And GenAI ML application is as much as easy as you already know JavaScript. Lot of modules come alive day by day making GenAI ML development further easy. There is no need of converting JSON to server side objects as in GenAI ML case both client and server runs on JavaScript.


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GenAI ML Course Modules

GenAI and It's Industry Applications

  •  Introduction to Generative AI.
  • AI vs ML vs DL vs NLP vs Generative AI.
  • Generative AI principles.
  • What is the role of ML in Gen-AI.
  • Different ML techniques (Supervised, Unsupervised, Semi-supervised & Reinforcement Learning).
  • Applications in various domains.
  • Ethical considerations.

NLP & Deep Learning

  • NLP essentials.
  • Basic NLP tasks.
  • Different text classification approaches.
  • Frequency-based – Bag of words, TF-IDF, N-gram.
  • Distribution Models – CBOW, Skipgram(Traditional approaches)and
    word2vec, Glove.
  • Ensemble Methods (Random Forest, Gradient Boosting, AdaBoost) &
    Traditional Machine Learning Models – Naïve Bayes, Support Vector
    Machine (SVM), Decision Trees, Logistic Regression.
  • Deep learning techniques – CNNs, RNNs, LSTMs, GRU and
    Transformers.

Generative AI Models

  • Autoencoders.
  • VAE’s and applications.
  • GANs and it’s applications.
  • Different types of GANs and applications.

Language Models & Transformer Models

  •  Different types of Language models
  •  Applications of Language models
  • Transformers and its architecture
  •  BERT, RoBERTa, GPT variations
  • Applications of transformer models

Prompt Engineering

  •  What is Prompt Engineering
  • What are the different principles of Prompt Engineering
  • Types of Different Prompt Engineering Techniques
  • How to Craft effective prompts to the LLMs
  •  Priming Prompt
  • Prompt Decomposition

Large Language Models

  • Generative AI lifecycle
  • What is RLHF
  •  LLM pre-training and scaling
  • Different Fine-Tuning techniques

LLM's Embeddings

  •  What are word embeddings
  •  What is the use of word embeddings, where we can use it?
  •  Word Embeddings – Word2Vec, GloVe and FastText
  • Contextual Embeddings – ELMo , BERT and GPT
  • Sentence Embeddings – Doc2Vec, Infersent, Universal Sentence
    Encoder
  • Subword Embeddings – BPE(Byte Pair Encoding), Sentence Piece
  •  Usecase of Embeddings.

Different Chunk Metrics

  •  What is Chunking
  • What is the use of chunking the document
  • What are the traditional effective chunking techniques
  • What are the problems and limitations with traditional chunking
    techniques?
  • How to overcome the limitations of Traditional chunking
  • Advanced Chunking Techniques:
    1. Character Splitting
    2. Recursive Character Splitting
    3. Document based Chunking
    4. Semantic Chunking
    5. Agentic Chunking

RAG and Advanced RA with Langchain

  •  What is RAG
  • What are the main components of RAG
  • High level architecture of RAG
  • How to Build RAG using external data sources
  • Advanced RAG

Langchain for LLMs

  • What is Langchain
  • What are the core concepts of Langchain
  • Components of Langchain
  • How to use Langchain agents

Vector Databases

  • LlamaIndex
  • What are Vector Databases
  • Why do we prefer Vector Databases over Traditional Databases
  • Different Types of Vector Databases: OpenSource and Close Source
  • OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
  • Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based Solutions

Finetuning LLMs

  • Supervised Finetuning
  • Repurposing-Feature Extraction
  • Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA

LLMs Evaluation

  • Text based LLMs:
    Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
    Score.
    Human Evaluation: Coherence, Factuality, Originality, Engagement
  •  Image based LLMs:
    Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
    Distance), IS (Inception Score), Perceptual Quality Metrics,
    Diversity Metrics.
    Human Evaluation: Photorealism, Style, Creativity, Cohesiveness
  •  Audio generation LLMs:
    Automatic Evaluation: FAD (Frechet Audio Distance), IS (Inception
    Score), Perceptual Quality Metrics – PAQM, PAQM – SNR (Signal-to-Noise Ratio), PAQM – PESQ (Perceptual Evaluation of Speech
    Quality)
    Human Evaluation:Perceptual Quality – PQ, PQ- Naturalness, PQFidelity, PQ- Musicality, Task Specific Evaluation.
  • Video Generation LLMs:
    Automatic Evaluation: FVD (Frechet Video Distance), Inception
    Score(IS), Perceptual Quality Metrics, Motion Based Metrics –
    Optical Flow Error, Content-Specific Metrics.
    Human Evaluation: Visual Quality, Temporal Coherence, Content
    Fidelit.

LLMops

  • Model Deployment and Management
  •  Scalability and Performance Optimization
  • Security and Privacy
  • Monitoring and Logging
  • Cost Optimization
  • Model Interpretability and Explainability.

LLM's on Cloud

  • Amazon Bedrock, Azure OpenAI

Different AI Tools

  • ChatGPT, Gemini, Copilot

Call on 9347045052 for complete details about the course
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