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AUTO-GPT & Pandas for ( NLP ) training course

In this course, we delve into the powerful tools and techniques that are revolutionizing the way we analyze and understand textual data. FromGPT & AUTO-GPTtoLangchainandPandas, we will explore how these cutting-edge technologies can be harnessed for tasks such as text classification, sentiment analysis, and named entity recognition.

JBI training course London UK

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"

Brian Leek, Data Analyst, May 2022

Public Courses

04/11/24 - 2 days
£2500 +VAT
16/12/24 - 2 days
£2500 +VAT
27/01/25 - 2 days
£2500 +VAT

Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK

AUTO-GPT & Pandas for ( NLP ) Training: 

  • Foundations of Natural Language Processing (NLP)
  • Explore The core concepts, techniques and challenges in NLP
  • Text preprocessing, feature extraction and text representation
  • Learn Basics of language models
  • Learn The role of language models in NLP
  • Explore GPT and AUTO-GPT, their architecure & how to use them for NLP 
  • Data Pre-Processing techniques and classification
  • How to clean and preprocess test data with Pandas and other libraries
  • How to tokenize, stem, lemmatize and handling of special characters
  • Training and fine tuning of language models for text classification tasks

Unit 1: Foundations of Natural Language Processing (NLP)

  • 1.1 The core concepts, techniques and challenges in NLP
  • 1.2 Text preprocessing, feature extraction and text representation

Unit 2: Basics of language models

  • 2.1 The role of language models in NLP
  • 2.2 GPT and AUTO-GPT, their architecure and how to use them for NLP tasks

Unit 3: Data Pre-Processing techniques and classification

  • 3.1 How to clean and preprocess test data with Pandas and other libraries
  • 3.2 How to tokenize, stem, lemmatize and handling of special characters
  • 3.3 Training and fine tuning of language models for text classification tasks: sentiment analysis, topic classification and spam detection
  • 3.4 Text classification, labeled datasets, model train and model performance evaluation

Unit 4: Sentiment analysis

  • 4.1 How to leverage language models for sentiment analysis
  • 4.2 Methods to perform sentiment analysis using pre-trained models and training custom models

Unit 5: Named Entity Recognition (NER)

  • 5.1 What is NER
  • 5.2 Hot to extract named entities from text data using language models
  • 5.3 Fine-tune models for better NER performance

More: 

Unit 1: Foundations of NLP and Language Models

  • 1.1 Understand the basics of Natural Language Processing (NLP)
  • 1.2 Explore the fundamentals of language models
  • 1.3 Learn about GPT and AUTO-GPT architecture
  • 1.4 Discuss the applications of language models in NLP

Unit 2: Text Preprocessing and Feature Extraction

  • 2.1 Perform data preprocessing using Pandas and other relevant libraries
  • 2.2 Learn techniques for tokenization, stemming, and lemmatization
  • 2.3 Handle special characters and noise in text data
  • 2.4 Extract relevant features from text for NLP tasks
  • Unit 3: Text Classification and Sentiment Analysis
  • 3.1 Dive into text classification using language models
  • 3.2 Train and fine-tune models for sentiment analysis
  • 3.3 Perform sentiment analysis on textual data
  • 3.4 Evaluate and interpret the results of sentiment analysis

Unit 4: Named Entity Recognition and Text Generation

  • 4.1 Understand named entity recognition (NER) and its importance
  • 4.2 Fine-tune models for named entity recognition tasks
  • 4.3 Extract named entities from text data
  • 4.4 Explore text generation techniques using language models

Unit 5: Real-world NLP Applications and Ethical Considerations

  • 5.1 Apply NLP techniques to real-world datasets
  • 5.2 Develop end-to-end NLP workflows using GPT, Langchain, and Pandas
  • 5.3 Discuss ethical considerations in NLP, including bias and privacy concerns
  • 5.4 Learn about responsible data usage and best practices in NLP projects
JBI training course London UK

This course is intended for data scientists, machine learning engineers, and software developers who want to gain expertise in advanced natural language processing techniques.

The attendees should have a baseline understanding of Python programming, data analysis, and fundamental NLP concepts before taking this course.

The immersive curriculum will equip those interested in working with textual data and language models with the practical skills to apply cutting-edge NLP tools like GPT, AUTO-GPT, Langchain and Pandas to real-world applications.

 


5 star

4.8 out of 5 average

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"

Brian Leek, Data Analyst, May 2022



“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and  the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

 

 

JBI training course London UK

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From GPT and AUTO-GPT to Langchain and Pandas, we will explore how these cutting-edge technologies can be harnessed for tasks such as text classification, sentiment analysis, and named entity recognition.

Whether you are a data scientist, a software engineer, or a technology enthusiast, this course is designed to empower you with the knowledge and expertise needed to leverage NLP for real-world applications. With a hands-on approach and a focus on practical examples, we will guide you through the intricacies of these tools, enabling you to unlock valuable insights from vast amounts of text data.

Are you ready to take your NLP skills to new heights, wielding the sword of NLP to break down the barriers of textual data with these advanced techniques?

1. Who is the target audience for this course?

This course is tailored for data scientists, software engineers, and technology enthusiasts who want to expand their knowledge and proficiency in Natural Language Processing (NLP). Whether you're a beginner or an experienced professional, the course offers valuable insights into advanced NLP techniques and their practical applications.

2. What prerequisites are required to enroll in this course?

While there are no strict prerequisites, a basic understanding of NLP fundamentals and familiarity with programming languages such as Python will be beneficial. Participants with prior experience in data analysis or software development will find the course content more accessible, but individuals with a strong interest in NLP are also encouraged to enroll.

3. What topics will be covered in this course?

The course will delve into advanced NLP techniques and tools, including GPT models, AUTO-GPT, Langchain, and Pandas. Participants will learn how to apply these cutting-edge technologies to tasks such as text classification, sentiment analysis, and named entity recognition. Practical examples and hands-on exercises will be provided to reinforce learning and facilitate skill development.

4. How will this course benefit my career or personal development?

By completing this course, you will acquire the knowledge and skills necessary to leverage advanced NLP techniques for real-world applications. Whether you're looking to enhance your career prospects in data science or simply expand your expertise in NLP, this course will empower you to extract valuable insights from textual data and tackle complex NLP challenges with confidence.

5. What can I expect from the hands-on approach of this course?

The course adopts a hands-on approach, allowing participants to apply theoretical concepts to practical scenarios. Through interactive exercises and real-world examples, you will gain firsthand experience in using advanced NLP tools and techniques, enabling you to effectively analyze and process textual data in various domains.

6. Is this course suitable for beginners in NLP?

While some familiarity with NLP concepts is beneficial, beginners with a strong interest in NLP can also benefit from this course. The course content is designed to cater to individuals with varying levels of experience, providing foundational knowledge alongside advanced techniques to ensure comprehensive learning and skill development.

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