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Natural Language Processing (NLP) LangChain

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Zero to Master Program

Natural Language Processing (NLP) LangChain

The LangChain program is a powerful tool in Natural Language Processing (NLP) designed to enhance the capabilities of language models by linking them to a variety of data sources and processing modules. It leverages the strengths of large language models (LLMs) and integrates them with external knowledge bases, databases, and APIs to provide more accurate and contextually rich responses. LangChain facilitates the creation of complex NLP applications by providing a framework that supports modularity, extensibility, and ease of use. It enables developers to build applications that can handle tasks such as information retrieval, question answering, and conversational agents. Additionally, LangChain includes features for managing dialogue state, contextual memory, and dynamic response generation, making it a versatile tool for creating sophisticated NLP solutions. By connecting language models with structured and unstructured data sources, LangChain enhances the functionality and practicality of AI-driven language applications.

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COURSE FEE

₹30,000

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4.8

12K+ Learners enrolled

100 +

Duration(Hours)

350+

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This is where you embark on an amazing journey!

Most flexible program in the industry

Freedom to learn

Watch classes any time at your convenience

Cheat days

Catch up on the course when life is calling you elsewhere

Features that keep you going

A structured curriculum that makes learning easy

Practice code problems of varying difficulty

Engagement coach to keep you motivated

Compile & run in an integrated coding environment

Get doubts resolved in 30 mins

1:1 sessions over voice call & chat with our skilled teaching assistants

Industry leading mentors to help you grow

1:1 Mock interviews with resume and career guidance

Structured feedback to make you better

Get a chance to be referred to your mentors’ company

Experience a seamless job switch with hiring assistance

Skill-based hiring across all levels of experience

The results

110%

Average salary hike

7000+

Transitions to product companies

250+

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Frequently asked but seldom read questions

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

None! Whether you’re completely new to the job field or have had some exposure, Algoxfusion Certificate is the right program for you..

You can find the ‘Get Certified’ option at the end of the Lessons in the table of contents. it will be shown only after completing all the lessons with 100% progress.

Submissions are evaluated based on criteria such as correctness, completeness, critical thinking, and adherence to instructions, typically using rubrics or grading guidelines provided by the instructor.
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Meet the faculty legends that will make you legendary

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Praveen Kumar

Founder & Instructor

Praveen has a full stack development experience and professional instructor and trainer for Flutter, Data Science, Machine Learning and Python Programming. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations.

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Lenin Prakash

Co-Founder & Instructor

He is an expert in JavaScript & React (Front-end) and has worked on open-source projects like Firebug and Zulip. He has also served as a GCI (Google Code-In) Mentor with Zulip. In his previous role as a Software Engineer he has worked for Goibibo-MMT.

Course curriculum for the curious

  • -Overview of NLP and its applications
  • -Historical development and evolution of NLP
  • -Key challenges in NLP
  • -Overview of LangChain and its role in NLP
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  • -Introduction to LangChain
  • -Setting up the development environment
  • -Basic structure and components of LangChain
  • -First steps: Building a simple NLP pipeline with LangChain
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  • -Importance of text preprocessing
  • -Techniques for cleaning and normalizing text
  • -Tokenization methods and tools
  • -Implementing text preprocessing and tokenization in LangChain
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  • -Introduction to feature extraction
  • -Bag-of-words, TF-IDF, and word embeddings
  • -Overview of popular embedding techniques (Word2Vec, GloVe, FastText)
  • -Using embeddings in LangChain
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  • -Basics of text classification
  • -Common algorithms: Naive Bayes, SVM, Decision Trees
  • -Deep learning approaches: RNNs, LSTMs, CNNs
  • -Building and evaluating text classification models with LangChain
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  • -Understanding NER and its applications
  • -Techniques for NER: Rule-based, Machine Learning, Deep Learning
  • -Training and deploying NER models using LangChain
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  • -Introduction to sentiment analysis
  • -Approaches to sentiment analysis: Lexicon-based, Machine Learning, Deep Learning
  • -Building and evaluating sentiment analysis models with LangChain
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  • -Overview of language generation tasks
  • -Techniques for text summarization: Extractive and Abstractive
  • -Generating text with pre-trained models (GPT, BERT)
  • -Implementing language generation and summarization with LangChain
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  • -Basics of machine translation
  • -Statistical vs Neural machine translation
  • -Training and deploying translation models using LangChain
  • -Evaluating translation quality
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  • -Introduction to question answering
  • -Approaches: Retrieval-based, Generative
  • -Building QA systems with pre-trained models
  • -Implementing and fine-tuning QA systems in LangChain
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  • -Transformer models and their impact on NLP
  • -Fine-tuning pre-trained models for specific tasks
  • -Multi-task learning and transfer learning in NLP
  • -Using LangChain for advanced NLP techniques
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  • -Overview of deployment strategies
  • -Containerization with Docker
  • -Serving models with APIs
  • -Deploying NLP models using LangChain
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  • -Monitoring and maintaining NLP models
  • -Handling model drift and data drift
  • -Scaling NLP applications
  • -Case studies and best practices
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  • -Understanding bias in NLP models
  • -Fairness, accountability, and transparency
  • -Privacy and security concerns
  • -Ethical guidelines and frameworks
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  • -Defining the project scope and objectives
  • -Data collection and preprocessing
  • -Model development and evaluation
  • -Deployment and presentation of the final project
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