2025 – Understand ,Test ,Fine-tune AI Model with HuggingFace

سرفصل های دوره

Understand Transformer models and learn how to Test, Evaluate, and Train them with HuggingFace NLP library

1 - Introduction to Machine Learning

1 -Introduction

2 -What is Transformer

3 -Understanding Transformer Architecture

4 -How Transformer Works

5 -Natural Language Processing (NLP)

2 - Working with Natural Language Processing (NLP) Library

1 -Introduction to NLP Libraries

2 -Understanding HuggingFace library

3 -Exploring HuggingFace platform

4 -Understanding and working with Model Inferencing

5 -Installing and working with Transformer library in Windows 11

6 -Writing simple code to understand how AI model Tokenize a text

7 -Working with Transformer library with Google Colabs

8 -Installing Jupyter Notebook in Visual Studio code

9 -NLP - Sentiment Analysis with AI Models

10 -NLP - Fill Mask with AI Models

11 -NLP - Named Entity Recognition (NER) with AI Models

12 -NLP - Question and Answering (Q&A) with AI Models

13 -Source.zip

13 - Source code.html

3 - Understanding and working with Transformer libraries In-Depth

1 -Introduction

2 -Pipeline Under-the-hood with Model downloading

3 -Understanding Models Heads

4 -Working of Pipeline functions Behind the scenes as code

5 -Understanding Model Checkpoints

6 -Understanding Batching and Checkpoints

7 -Applying Padding and Truncation for Sequence classification of Model

4 - Various ways to work with LLM Models

1 -Introduction

2 -Accessing OpenAI APIs

3 -More about OpenAI APIs

4 -Working with Local LLMs

5 -Working with Local LLMs for Vision

6 -Using Local LLMs APIs and using in Postman

5 - Functional Testing of AI Models

1 -Introduction

2 -Temperature Testing of AI Models with OpenAI Playground

3 -Temperature Testing of AI Models with NLP library

4 -Repeatability Testing of AI Models

5 -Question and Answering Testing of AI Model

6 -Named Entity Recognition Testing of AI Model

7 -Multi-Model Testing of AI Model

6 - Bias and Fairness Testing of AI Models

1 -Introduction

2 -Bias Testing of AI Models with NLP library

3 -Bias Testing with Negative scenario

4 -Explainability Testing of AI Models

5 -Explainability Testing with NLP Libraries

6 -Section5.zip

6 - Complete Source.html

7 - Evaluating AI Models

1 -Introduction

2 -Evaluation of Unseen Data using NLP library

3 -Evaluation of Question and Answering Model using NLP Library

4 -Evaluating Q&A Model with Fuzzy Matching

5 -Section6.zip

5 - Complete Source.html

8 - Working with AI Agentic Agents

1 -What is AI Agents

2 -Spectra of AI Agents

3 -Writing AI Agents with SmolAgents

4 -Write UI Operation with AI Agents on behalf of User via Selenium & SmolAgent

5 -Introduction to Model Context Protocol (MCP) for AI Agents

6 -MCP Server in Action for UI Action via Playwright

7 -Configuring MCP Server and working with them

8 -Section7.zip

8 - Complete Source.html

9 - Fine-Tuning and Training AI Model

1 -Introduction

2 -Understand how to Fine-tune a Model

3 -Understanding Large Dataset to Train the model

4 -Setting Environment ready with Jupyter Notebook

5 -Step 1 Importing Dataset from HuggingFace

6 -Analysis Dataset for better understanding

7 -Step 2 Perform Tokenizer by converting Text to Tokens

8 -Step 3 Split Test, Train and Validation Data

9 -Create Dataset in DatasetDict Format with Training data

10 -Step 4 Tokenize all data from Dataset

11 -Understanding Base Model and using it with AutoModel

12 -Step 5 Using AutoModelForSequenceClassification for Model Head

13 -Step 6 Creating Training Arguments

14 -Step 7 Create Compute Metrics for Model Evaluation

15 -Step 8 Creating and Running Trainer to train the model

16 -Understand Trainer Metrics after Training is complete

17 -Using our Fine-Tuned Model from Local machine and Inference via Pipeline

18 -Upload our Fine-tuned Model to HuggingFace and use it from HuggingFace

19 -TrainingModel.zip

19 - Complete Source.html

10 - Training DistilBert Model for Q&A Classification using IMDB Movie Dataset

1 -Introduction

2 -Load IMDB Dataset from CSV

3 -Create Q&A Context structure

4 -Creating SQuAD format for the dataset

5 -Converting SQuAD to HuggingFace DataSet

6 -Creating TestTrainValidation set from Dataset

7 -Tokenize Data from TestTrainValidation dataset

8 -Creating TestTrainValidation format for DatasetDict with Tokenized value

9 -Create Training Argument and Trainer and start training of model

10 -Saving Model locally & using Local model for Inferencing with Q&A Classification

11 -TrainingModels.zip

11 - Source code.html

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تولید کننده: Udemy