Generative AI - Natural Language Processing Bootcamp 2025

[0dayddl]

10.23 GB | 1h 13min 41s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Why NLP and how its different from Normal ML.mp4 (21.43 MB)
3 Understanding Human Language.mp4 (20.9 MB)
5 Challenges of NLP.mp4 (36.66 MB)
7 Summary.mp4 (5.07 MB)
10 Hands On Spacy.mp4 (95.57 MB)
11 Summary.mp4 (6.64 MB)
2 NLP Pipeline.mp4 (72.26 MB)
3 Data Extraction and Text Cleaning hands On.mp4 (185.96 MB)
4 Introduction to NLTK library.mp4 (38.18 MB)
5 Tokenization , bigrams, trigrams, and N gram - Hands on.mp4 (18.52 MB)
6 POS Tagging & Stop Words Removal.mp4 (51.78 MB)
7 Stemming & Lemmatization.mp4 (84.04 MB)
8 NER and Wordsense Disambiguation.mp4 (90.06 MB)
9 Introduction to Spacy Library.mp4 (32.16 MB)
PNLP S02V02 bookscraper.zip (349 B)
2 Vector Representation of Text - One Hot Encoding.mp4 (93.21 MB)
3 Understanding BoW Technique.mp4 (62.25 MB)
4 BoW Hands On.mp4 (94.57 MB)
5 TF-IDF.mp4 (97.1 MB)
6 TF-IDF Hands On.mp4 (133.36 MB)
7 Tf-idf from Scratch Implementation.mp4 (71.27 MB)
10 Practical Difference between CBOW and Skip-gram.mp4 (33.6 MB)
11 Bonus How does a Network is trained - Back-propagation.mp4 (112.56 MB)
12 Section Summary.mp4 (3.86 MB)
2 Introduction to Word Embeddings.mp4 (77.37 MB)
3 Intuition of Vector Representation.mp4 (98.21 MB)
4 Hands On Word Embeddings - Usage of Pre-trained models.mp4 (188.39 MB)
5 Skip-gram Word Embeddings - Understanding Data Preperation.mp4 (44.21 MB)
6 Skip Gram Model Architecture.mp4 (93.33 MB)
7 Skip Gram Hands On - Deep Dive.mp4 (223.81 MB)
8 CBOW Model Architecture & Hands On.mp4 (47.18 MB)
9 Hyperparameters - Negative Sampling and Sub Sampling.mp4 (133.05 MB)
10 Challenges of NLP & N-grams.mp4 (62.77 MB)
2 General Pipeline for Classification.mp4 (71.74 MB)
3 Approaches to Classification.mp4 (62.25 MB)
4 Loading the Dataset.mp4 (35.77 MB)
5 Exploratory Data Analysis & Text Preprocessing.mp4 (75.93 MB)
6 Remove Low Frequency Words.mp4 (47.1 MB)
7 Remove Stop Words with Stemming & Lemmatisation.mp4 (73.32 MB)
8 Application of Model.mp4 (73.59 MB)
9 TfIDF Approach.mp4 (31.59 MB)
1 Introduction to NER.mp4 (61.62 MB)
2 Understanding CRF - Introduction.mp4 (48.38 MB)
10 Create Dialogflow chatbot.mp4 (200.72 MB)
11 Dialogflow Fulfilment.mp4 (53.19 MB)
12 Dialogflow IntegrationsDeployment.mp4 (32.58 MB)
13 Dialogflow Miscellaneous Tools.mp4 (54.81 MB)
2 Understanding Chatbots.mp4 (43.95 MB)
3 Building a Simple Chatbot.mp4 (27.95 MB)
4 Hands On Building a Simple FAQ Chatbot.mp4 (90.7 MB)
5 Types of Chatbot and Pipeline for Chatbot.mp4 (37.82 MB)
6 Terminologies in Chatbot.mp4 (21.54 MB)
7 Dialog flow - Introduction.mp4 (61.05 MB)
8 Basics of Dialogflow.mp4 (142.08 MB)
9 Dialogflow system setup.mp4 (122.5 MB)
learn-agent-fulfilment.zip (635 B)
learn-agent.zip (622 B)
2 Deep Dive into the components of Dialog System.mp4 (37.31 MB)
3 Dialog Intent Prediction.mp4 (9.56 MB)
4 Deep Learning based intent Classification.mp4 (180.72 MB)
10 Working with Action File - Demo.mp4 (185.19 MB)
11 Building Custom Action File.mp4 (93.33 MB)
12 Test the Action Server.mp4 (83.2 MB)
13 RASA Pipeline file.mp4 (123.98 MB)
14 RASA Deployment - Integration with RASA Chatbot - Pre-requisites.mp4 (18.36 MB)
15 Run Ngork on RASA Chatbot with Actions.mp4 (41.79 MB)
16 Slack Settings for Connection to RASA Chatbot.mp4 (119.64 MB)
17 Practice Project Concert Chatbot & Summary.mp4 (40.73 MB)
2 Introduction to RASA Chatbot.mp4 (28.46 MB)
3 Installation of RASA.mp4 (109.72 MB)
4 RASA project Structure.mp4 (81.42 MB)
5 RASA Files.mp4 (44.99 MB)
6 Basics of YAML.mp4 (89.82 MB)
7 Building the chatbot - Add intents and Response.mp4 (148.3 MB)
8 Building the chatbot - Extract Entity & working with Slots.mp4 (185.57 MB)
9 Create API Key from NyTimes.mp4 (52.81 MB)
2 Text Summarization - Introduction.mp4 (10.37 MB)
3 Hands On Text Summarization.mp4 (128.89 MB)
2 Importance of Social Media Platforms.mp4 (15.67 MB)
3 Setting Up Twitter Developer Account.mp4 (22.21 MB)
4 Introduction to Tweepy.mp4 (99.7 MB)
5 Hands On Implementation of Project.mp4 (186.43 MB)
1 NLP Transformers - Introduction.mp4 (45.69 MB)
2 Feed Forward Neural Network and Challenges.mp4 (152.41 MB)
3 RNN - Recurrent Neural Networks.mp4 (146.11 MB)
4 LSTM - Long Short Term Memory Networks.mp4 (70.91 MB)
5 Attention Mechanism - Attention is all you Need.mp4 (127.91 MB)
6 Transfer Learning.mp4 (68.02 MB)
7 Transformer Architecture Overview.mp4 (38.43 MB)
8 Deep Dive into Attention Layers.mp4 (546.82 MB)
9 Additional Video on Transformers.mp4 (41.17 MB)
2 Introduction to Hugging Face Library.mp4 (40.29 MB)
3 Working with Hugging Face Library Pipeline.mp4 (144.98 MB)
4 Text Classification with HuggingFace Transformers - Data Loading.mp4 (171.78 MB)
5 Tokenization using Huggingface.mp4 (117.04 MB)
6 Tokenization on Dataset.mp4 (74.95 MB)
7 Text Classification with Feature Extraction.mp4 (181.65 MB)
8 Finetuning on Transformers.mp4 (58.68 MB)
1 Working with ChatGPT-3.mp4 (116.78 MB)
1 Working of BERT Language Model.mp4 (192.91 MB)
1 Introducing Prompt Engineering and Generative AI ChatGPT.mp4 (683.33 MB)
1 Installation of Anaconda.mp4 (44.73 MB)
3 Machine Learning Model Deployment Model Prep.mp4 (84.18 MB)
4 Deploy as Flask App.mp4 (94.87 MB)
5 StreamLit Deployment.mp4 (123.93 MB)
7 Introducing MLOps.mp4 (29.71 MB)
8 Bonus Introduction to Enterprise MLOps.mp4 (792.41 MB)

]
Screenshot

Spoiler

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

NitroFlare

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

Generative AI Bootcamp
Published 6/2025
Duration: 14h 4m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 5.91 GB
Genre: eLearning | Language: English​

Build Generative AI applications using LangChain, RAG. Build multi agentic AI systems using Crew AI. Master LLMs.

What you'll learn
- Learn to build Generative AI applications using LangChain. Understand how to use LangChain components.
- Learn to build multi agentic systems using Crew AI and LangChain tools. Deep dive different components of Crew AI.
- Learn to build Retrieval-Augmented Generation (RAG) pipelines - preparing input, chunking methods, embeddings, vector store, similarity search, RAG pipeline
- Learn prompt engineering techniques with practical implementation - Basic, Role Task Context, Few shot, Chain of thought, Constrained Output Prompting
- Learn chains with practical implementation - Single, Simple Sequential, Sequential, Math, RAG, Router, LLM Router, SQL Chains and many more
- Learn document Loaders with practical implementation - CSVLoader, HTMLLoader, PDFLoaders and many more
- Learn Hugging Face and how to use the models from Hugging Face and build Generative AI applications
- Learn different Text Chunking Methods in RAG Systems - Character Text Splitter, Recursive Character Text Splitter, Markdown Header, Token Text Splitter Chunking
- Learn vector Databases for RAG Systems: Pinecone, Chroma, Weaviate, Milvus, FAISS
- Understand the terminology - Artificial intelligence, Machine Learning, Deep Learning and Generative AI.
- Understand the attention mechanism and how transformers encode and decode data.
- Understand Foundation Models, history, Applications, types, examples of foundation models.
- Understand Language Model Performance; Top Open-Source LLMs; How to Select the right Foundation Model. And, responsible AI practices and the importance of addre
- Learn memory types with practical implementation - ConversationBufferMemory, Conversation Buffer Window, ConversationSummaryMemory and many more

Requirements
- We cover Python basics but prefer to have familiarity with the Python programming language.
- Access to a computer with good internet connection.
- Have access to OpenAI, Claude Anthropic, or you can use open source models
- Basic understanding on using different code editors - Jupyter notebook, VScode, etc.

Description
Learn how to download and install Anaconda Distribution, Jupyter notebook, Visual Studio Code

Learn how to use Jupyter notebook 'Markdown' features

Learn how to install CUDA Toolkit, cuDNN, PyTorch and how to enable GPU

Learn Python basics - Introduction, Package Installation, Package Import, Variables, Identifiers, Type conversion, Read input from keyboard, Control statements and Loops, Functions, string, Data Structures - list, tuple, set, dict

Learn what is Artificial intelligence, Machine Learning, Deep Learning and Generative AI; And, the history of AI;

Understand the attention mechanism and how transformers encode and decode data

Understand what are the Foundation Models, history, Applications, types, examples of foundation models.

Understand Language Model Performance; Top Open-Source LLMs; How to Select the right Foundation Model?

Learn Responsible AI practices and the importance of addressing biases

Learn how to build Generative AI applications Using LangChain, RAG

Learn what is RAG(Retrieval-Augmented Generation) and deep dive on preparing input, chunking methods, embeddings, vector store, similarity search, RAG pipeline

Understand Vector Databases for RAG Systems: Pinecone, Chroma, Weaviate, Milvus, FAISS

Learn different Text Chunking Methods in RAG Systems and how to choosing a chunking Method

Character Text Splitter Chunking Method

Recursive Character Text Splitter Chunking Method

Markdown Header Text Splitter Chunking Method

Token Text Splitter Chunking Method

Learn what is Prompt Engineering

Learn how to create OpenAI account and how to generate API key

Learn different prompt engineering techniques

Basic prompt

Role Task Context Prompt

Few shot Prompting

Chain of thought Prompting

Constrained Output Prompting

Understand Document Loaders - CSVLoader, HTMLLoader, PDFLoaders

Learn how to provide memory to Large Language Models(LLM)

Learn different memory types - ConversationBufferMemory, Conversation Buffer Window, ConversationSummaryMemory

Learn how to chain different LangChain components

Learn different chains - Single Chain, Simple Sequential Chain, Sequential Chain, Math Chain, RAG Chain, Router Chain, LLM Router Chain, SQL Chain

Learn how to build multi agentic frameworks using CrewAI and LangChain tools

Learn what is Hugging Face and how to use the models from Hugging Face and build Generative AI applications

Who this course is for:
- Developers interested in building Generative AI applications using LangChain, RAG.
- Programmers interested in building multi agentic frameworks.
- AI engineers and data scientists.

Bitte Anmelden oder Registrieren um Links zu sehen.

Please check out others courses in your favourite language and bookmark them
- - - -

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

NitroFlare

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

DDownload

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!