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NLP – Syllabus
NLP – Syllabus Table Of Contents: Introduction To NLP. NLP Tools & Libraries. NLP Data Formats. NLP Pipeline. Text Preprocessing Steps. Regular Expression In NLP. Embedding Techniques. Sequence Modeling. Transformers and Pre-trained Models. Evaluation Metrics Advanced NLP Tasks. Real-World NLP Projects Ethical Considerations (1) Introduction To NLP. What is NLP ? Real-World Applications of NLP . Challenges in NLP . Differences Between NLP, NLU, and NLG . Rule-Based vs Statistical vs Neural NLP (2) NLP Tools & Library NLTK (Natural Language Toolkit) spaCy (Fast, production-ready NLP tasks) TextBlob (Simpler NLP tasks) Gensim (Topic modeling & word embeddings) Flair (Zalando) (Sequence
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Transformer – Interview Question & Answer
Transformer – Interview Questions Table Of Contents: Basic Understanding Attention Mechanism Model Architecture Training Transformer Model Prediction Transformer Model Interpretability & Optimization Practical/Advanced Concepts Beyond Vanilla Transformers (1) Basic Understanding What is the Transformer model and why was it introduced? What are the key components of a Transformer? What does the term “attention” mean in NLP? How is a Transformer different from an RNN/LSTM? What are the main advantages of Transformers over RNNs? What is positional encoding, and why is it needed in Transformers? Can you explain how a Transformer model works? What are Query, Key, and Value in the
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Transformers – Syllabus
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Generative AI – Syllabus
Generative AI – Syllabus Table Of Contents: GenAI Road Map For Beginners. GenAI Using Langchain. Introduction To Langchain. Langchain Components. Langchain Models. Prompts In Langchain. Structured Output In Langchain. Output Parser In Langchain. Chains In Langchain. What Is Runnable In Langchain? Langchain Runnables. Document Loaders In Langchain. Text Splitter In Langchain. Vector Stores In Langchain. Retrievers In LangChain. Retrieval Augmented Generation. Building RAG system In LangChain. Tools In LangChain. Tool Calling In LangChain.
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AgenticAI – Customer Service Agent
Agentic AI – Customer Service Agents Table Of Contents: What Is Customer Service Agents ? What Tasks Customer Service Agents Can Perform ? Steps To Build Customer Service Agents. Python Implementation. (1) What Is Customer Service Agents ? (2) What Tasks Customer Service Agents Can Perform ? (3) Steps To Implement Customer Service Agents. (4) Python Implementation (1) Set Up the Environment pip install openai langchain pymilvus tiktoken (2) Inject & Embed Support Document from langchain.document_loaders import DirectoryLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from pymilvus import Collection, connections from langchain.vectorstores.milvus import Milvus # 1. Connect to Milvus
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AgenticAI – Understanding Agentic AI ?
Agentic AI – Understanding Agentic AI . Table Of Contents: What Is Agentic AI ? Key Features of Agentic AI. How Is It Different From Regular AI? Examples Of Agentic AI. How To Mention The Goals Of The AgenticAI Model ? (1) What Is Agentic AI ? Agentic AI is a type of smart computer program that can think for itself, make decisions, and take actions to reach a goal—kind of like a little robot brain with a mission. (2) Key Features of Agentic AI: (3) How Is It Different From Regular AI? Regular AI waits for you to tell
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Agentic AI – Syllabus
Agentic AI Syllabus Table Of Contents Foundatation Of Agentic AI. User Interaction Layer Of Agentic AI. Input Validation & Guardrails Layer Of Agentic AI. Orchestration / Control Layer Of Agentic AI. Agent Selection & Role Assignment Layer Of Agentic AI. Reasoning & Planning Layer (Inside the Agent) Of Agentic AI. Knowledge Retrieval Layer (RAG) Of Agentic AI. Memory Layer (Context & Experience) Of Agentic AI. LLM (Model) Layer — Intelligence Core Of Agentic AI. Tool & Action Layer (Execution) Of Agentic AI. Observation & Feedback Layer Of Agentic AI. Reflection & Iteration Layer Of Agentic AI. Output Validation & Safety Layer Of
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GenAI – Internal Q & A Model
Gen AI – Q & A Model Table Of Contents: Define The Problem Statement. Steps To Complete This Use Case. (1) Define The Problem Statement (2) Step-by-Step Guide to Build HR Policy Assistant (RAG-based) (3) Collect & Prepare HR Documents. (1) Install Required Libraries pdfplumber python-docx unstructured pip install pdfplumber python-docx unstructured (2) Load and Extract Text from Documents import os import pdfplumber from docx import Document def load_text_from_pdf(file_path): text = “” with pdfplumber.open(file_path) as pdf: for page in pdf.pages: text += page.extract_text() + “n” return text def load_text_from_docx(file_path): doc = Document(file_path) return “n”.join([para.text for para in doc.paragraphs]) def load_text_from_txt(file_path):
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Seaborn – Q – Q Plot
Linear Regression – Assumption- 1 (Linear Relationship) Table Of Contents: What Is Q – Q Plot ? Example Of Q – Q Plot . Why There Is A Straight Line In The Q – Q Plot ? (1) What Is Q – Q Plot ? A Q–Q plot (Quantile–Quantile plot) is a probability plot that compares the quantiles of a dataset to the quantiles of a theoretical distribution (often the normal distribution). It helps to visually check if your data is normally distributed. (2) When to use a Q–Q Plot ? To assess normality (Is my data normally distributed?) To
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Linear Regression – Assumption – 6 (How To Detect & Avoid Endogeneity ?)
