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Natural Language Processing: NLP, Gemini & Transformers
Rating: 4.6 out of 5(14 ratings)
835 students

Natural Language Processing: NLP, Gemini & Transformers

Explore NLP fundamentals, conversational AI, sentiment analysis and practical language applications with the Gemini API.
Created byDr. Chris Mall • 40,000+ Students Worldwide
Last updated 11/2024
English
English [Auto],

What you'll learn

  • Master Essential NLP Concepts: Understand the core principles of Natural Language Processing, including text preprocessing, tokenization, and language models.
  • Build Real-World NLP Applications: Gain hands-on experience developing NLP solutions like sentiment analysis, text classification, and named entity recognition.
  • Work with Transformer Models: Learn to implement and fine-tune state-of-the-art transformer models such as ChatGPT and Gemini for advanced NLP tasks.
  • Apply NLP in Practical Scenarios: Develop the skills to apply NLP techniques to real-world challenges in business, research, and build a portfolio of projects.

Course content

3 sections • 17 lectures • 4h 15m total length

  • Introduction to Artificial Intelligence and Natural Language Processing2:11

    Explore artificial intelligence foundations, including expert systems, NLP, speech recognition, and machine vision, and the four AI types—reactive, memory, theory of mind, and self-awareness.

  • Let's see Artificial Intelligence in Practice16:45

    Demonstrate training image, sound, and pose models with Teachable Machine, test in real time, export to web or Android, and explore key ai tools for NLP and beyond.

  • 3 stories for how AI began - Alan Turing - Gary Kasparof21:09

    Explore Alan Turing’s legacy, the Turing test, and milestones with Kasparov, Deep Blue, and AlphaZero, highlighting reinforcement learning, machine learning, and the evolution of natural language processing.

  • Introduction to Machine Learning10:45

    Explore how machines learn through training and testing, using supervised, unsupervised, and reinforcement learning to solve tasks like spam filtering, house-price prediction, clustering, and robotics.

  • An Agent for Psychologists17:56

    Explore how AI tools, including chatbots and virtual therapists, support psychologists with early detection, sentiment analysis, and personalized therapy from speech, text, and nonverbal cues.

  • Differences between Artificial Intelligence, Machine Learning and Data Science6:27

    Differentiate data science, machine learning, and artificial intelligence by examining data modeling, warehousing, and AI's use of deep learning and natural language processing.

  • Common Artificial Intelligence Workloads. Machine Learning, NLP, Computer Vision1:09

    Explore common AI workloads, including machine learning, anomaly detection, computer vision, NLP, and conversational AI, with a focus on interpreting data, patterns, and human language.

  • What is Natural Language Processing1:25

    Explore natural language processing and how Azure workloads enable machines to read, understand, and derive meaning from human language, with text analysis, entity recognition, sentiment analysis, and translation.

  • What is Conversational AI?2:47

    Explore the fundamentals of conversational AI, including dialogue with bots across text and voice channels, and the role of design, context, and intent understanding in human-like interactions.

Requirements

  • No Prior NLP Experience Required: This course is designed for beginners, so no prior knowledge of Natural Language Processing is needed.
  • Basic Python Knowledge: Familiarity with Python programming is helpful but not mandatory. We'll cover any necessary Python concepts along the way.
  • A Computer with Internet Access: You'll need a laptop or desktop computer to follow along with the coding exercises and install required libraries.
  • A Desire to Learn: Enthusiasm for exploring NLP and applying it to real-world problems is all you need to get started!

Description

Learn natural language processing (NLP) through an introduction to artificial intelligence, practical language examples, and an exploration of transformer models. This course connects the vocabulary of AI with the kinds of text-based applications you can investigate and build on as you develop your skills.


Start with the foundations: how artificial intelligence, machine learning, and data science relate to one another, what natural language processing does, and where conversational AI fits. The introductory lessons use examples and historical context to help you distinguish common AI workloads before moving into language-focused applications.


Next, explore the Gemini API and an emotional analysis example. Use these lessons to think about the input you provide, the output you expect, and how you would check whether an answer is useful. The later lessons introduce transformers, discuss ChatGPT-4o architecture, and walk through NLP application examples.


This course is suitable for learners exploring NLP, developers broadening their understanding of language applications, and students who want a structured introduction to the field. Basic programming familiarity will help you follow technical examples. Work through the demonstrations at your own pace, take notes, and repeat the examples with your own non-sensitive sample text.


AI services and interfaces evolve, so compare any setup steps and model availability with the provider's current documentation. The emphasis is on understanding the concepts and evaluating results, rather than assuming that every generated answer is correct. By the end, you will have a clearer foundation for choosing an NLP use case and planning your next practical experiment.

Who this course is for:

  • Beginners who are new to Natural Language Processing (NLP) and want to learn the fundamentals in a hands-on, practical way.
  • Developers and Data Scientists looking to expand their skill set by incorporating NLP into their projects and workflows.
  • AI Enthusiasts who are eager to explore how language data is processed and used to build intelligent applications like chatbots, sentiment analyzers, and more.
  • Entrepreneurs and Product Managers interested in integrating NLP technology into their products, enhancing user experiences, or automating text-based tasks.
  • Students and Researchers seeking to build a strong foundation in NLP to pursue advanced studies or contribute to academic and industry-related projects.

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