The Complete NLP & GPT-4 Course: Real-World Python Projects from Udemy

What's inside

Learning objectives

All lectures have subtitle options: english, mandarin, spanish, french, hindi, arabic
Project: make a recipe generator as you learn to get, install and use openai's api & gpt-4 with python
Project : create an interactive storyteller application with gpt-4, using openai api with python. understand how gpt works: apply the math of self-attention
Project: create personalized marketing campaigns with python and gpt-4
Project : calculate financial risk for investing in companies like tesla, nio, by analyzing market sentiment with news data. apply ner and spacy models
Project: scrape a website using beautiful soup to gather data and create your own dataset of book reviews
Project: custom chatbot for online bookstore using openai api and gpt-4. train your chatbot on the scraped data that you obtained from the project before
Project: make a travel itinerary to learn about rag++ & llm integration, with python
Project: netflix recommendation system
Project: use spacy in a name entity recognition practical, using python in google colab
Get accredited badges to use on linkedin to showcase skills in python, nlp, sentiment analysis, gpt, transformers, hugging face
Learn to craft personalized marketing content using python and gpt-4, creating tailored messages that resonate with different customer personas for campaigns

Learn the logic & math formulas behind gpt
Master nlp fundamentals and advanced transformer models like gpt-4 to power real-world ai solutions.
Use openai api to create interactive chatbots and generate engaging content.
Libraries: hugging face, nltk, spacy, keras, sci-kit learn, tensorflow, pytorch
Deep learning: neural networks, rnn, lstm theory & practical projects
Cosine-similarity & vectors
A python guide chapter for beginners - learn python fundamental basics like: what is a function, a library
No tedious anaconda or jupyter installs: use modern google colab cloud-based notebooks for using python
Linguistics foundation to help learn nlp concepts
Use matplotlib to output a visual graph to illustrate financial risk of investing in companies
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All lectures have subtitle options: english, mandarin, spanish, french, hindi, arabic
Project: make a recipe generator as you learn to get, install and use openai's api & gpt-4 with python
Project : create an interactive storyteller application with gpt-4, using openai api with python. understand how gpt works: apply the math of self-attention
Project: create personalized marketing campaigns with python and gpt-4
Project : calculate financial risk for investing in companies like tesla, nio, by analyzing market sentiment with news data. apply ner and spacy models
Project: scrape a website using beautiful soup to gather data and create your own dataset of book reviews
Project: custom chatbot for online bookstore using openai api and gpt-4. train your chatbot on the scraped data that you obtained from the project before
Project: make a travel itinerary to learn about rag++ & llm integration, with python
Project: netflix recommendation system
Project: use spacy in a name entity recognition practical, using python in google colab
Get accredited badges to use on linkedin to showcase skills in python, nlp, sentiment analysis, gpt, transformers, hugging face
Learn to craft personalized marketing content using python and gpt-4, creating tailored messages that resonate with different customer personas for campaigns
Learn the logic & math formulas behind gpt
Master nlp fundamentals and advanced transformer models like gpt-4 to power real-world ai solutions.
Use openai api to create interactive chatbots and generate engaging content.
Libraries: hugging face, nltk, spacy, keras, sci-kit learn, tensorflow, pytorch
Deep learning: neural networks, rnn, lstm theory & practical projects
Cosine-similarity & vectors
A python guide chapter for beginners - learn python fundamental basics like: what is a function, a library
No tedious anaconda or jupyter installs: use modern google colab cloud-based notebooks for using python
Linguistics foundation to help learn nlp concepts
Use matplotlib to output a visual graph to illustrate financial risk of investing in companies
Show more
Show less

Syllabus

Introduction

Learn about NLP & Data Science in the real world; Differentiate between Data Science, NLP, Machine Learning & A.I

Get an idea of what NLP is as well as the job and salary options. In addition, learn to differentiate between NLP, Data Science, Machine Learning & A.I.

Learn the basics of NLP, get an overview of the logical workflow - a big picture understanding. Starting with word embeddings, cosine similarity, and an introduction to attention—key concepts that lay the foundation for understanding transformers like GPT.

Purpose: This short quiz is designed to reinforce key concepts covered in the chapter on the NLP pipeline, including text preprocessing, normalization, and word embeddings.

A warm welcome to the math behind GPT! This brief intro encourages beginners, reassuring them that we'll use easy-to-follow explanations, visuals, and hands-on examples to make the concepts accessible and enjoyable.

Get a high-level overview of how GPT works! Imagine GPT as an orchestra, with each math concept as an instrument. Learn how all components come together in harmony to generate meaningful language.

Understand multi-head attention, embeddings, and gradient descent with engaging analogies. Learn how each plays a unique role, like conductors, musical notes, and practice sessions, to bring GPT's symphony together.

Discover how word embeddings relate like friendships through cosine similarity, and how self-attention adds depth, deciding who to listen to in a conversation based on shared context.

Learn about high vs low attention in GPT using a sandwich analogy. Understand why some ingredients are key, while others receive less focus, depending on their importance in creating the final result.

Understand self-attention through an engaging library analogy. Learn how Query (Q), Key (K), and Value (V) work together like finding the best book in a library based on your question and book details.

Explore the scaling factor in self-attention using a classroom analogy. Learn how dividing the attention helps stabilize learning—like a teacher ensuring no student gets overwhelmed by too much information.

Understand how self-attention helps GPT decide word relevance using a story analogy. Learn how queries, keys, values, and Softmax work together to highlight key ideas, like deciding which parts of a sentence are crucial to continue a narrative.

Learn how GPT uses self-attention and probabilistic modeling to generate vivid next words, such as "sizzling grill" and "flavor." See how context influences the continuation of sentences in a natural, sensory-driven way.

Learn how to create and configure a new Colab notebook. This foundational step ensures you're ready to write and execute Python code, connect to the cloud environment, and start building your projects.

Learn how to open .IPYNB files in Google Colab and locate the provided course resource folders. This step ensures you have easy access to all necessary materials for seamless coding practice.

Learn how to customize your Google Colab environment by adjusting settings like dark vs. light mode and other preferences. Personalize Colab for comfort and efficiency during your coding sessions.

Learn how to install the OpenAI library and import essential Python libraries. This step is needed for setting up your environment to interact with GPT-4 and start building AI-powered projects effectively.

Learn how to obtain your OpenAI API key. This key is essential for authenticating and accessing GPT-4 services, enabling you to create and interact with AI-powered applications like the recipe generator.

Learn how to create a list of ingredients for the recipe generator project. This step will help you provide input to GPT-4, enabling the model to generate creative recipes based on your chosen ingredients.

Learn how to generate three random ingredients from your list. This step will provide diverse inputs for GPT-4, encouraging creative and unique recipe outputs as you interact with the OpenAI API.

Learn how to define a Python function to generate creative recipes using GPT-4. See how to prompt the AI like a helpful chef and use OpenAI's API to turn ingredients into delicious dishes.

Learn how to call the promptGPT function to generate a recipe from selected ingredients using GPT-4. See how to display the AI's creative output in an engaging way for your culinary adventure.

Overview of the interactive storytelling project with GPT-4. Discover how you’ll guide the main character, Elara through adventures, explore creative generation, and see how math concepts like Attention are applied in storytelling

Quickly set up your interactive storytelling project by installing required libraries and inserting your OpenAI API key in Google Colab. A simple guide to get you ready for adventure!"

Build a function that generates creative story content using GPT-4. We'll guide you through setting up AI roles, user prompts, and extracting story responses—all within Google Colab.

Kick off your interactive storytelling adventure! Learn how to set up an engaging story prompt, guide GPT-4 using system requirements, and watch as the AI continues your narrative creatively

Learn how to split a growing story into chapters, just like a series of episodes. Understand how GPT-4 maintains consistency and engagement by leveraging attention across these dynamic story segments

Discover how to split a long story into manageable chapters, like a Netflix series. Learn how GPT-4 keeps each chapter coherent and consistent using attention, creating an engaging, interactive narrative

Add interactivity to your story! Use Python to prompt users for input to decide Elara's next move—whether she goes left, right, or climbs a tree. Engage users and make your storytelling dynamic

See how GPT-4 uses user input to generate the next part of the interactive story. Whether Elara ventures into a dark cave or climbs a mysterious tree, watch how the story dynamically evolves

Learn how to add each newly generated part of the story as a new chapter, keeping the adventure organized and easy to follow. Keep track of Elara's journey chapter by chapter.

Decide if you want to keep the adventure going! In this step, you'll choose whether to continue Elara's journey, adding more excitement to the story. Join me and my co-author, Harley, for more fun!"

Add various user-driven actions like exploring, interacting with characters, or making bold decisions. This will make Elara's story more engaging and dynamic as you follow along

Watch how your choices influence Elara's journey. We'll review the generated story output based on the decisions made, showcasing the power of interactive storytelling with GPT-4.

Import and utilize essential libraries like Pandas, SpaCy, and Hugging Face to scrape, analyze, and visualize financial data using sentiment analysis and entity recognition.

Understand how to define a Python function using requests and BeautifulSoup to retrieve financial news headlines from Google, focusing on targeted company-specific queries.

Learn how to use the get_news function to retrieve and display financial news headlines for multiple companies, preparing the data for further sentiment analysis and risk assessment.

Understand how pre-trained models like DistilBERT are fine-tuned for sentiment analysis. Learn to use Hugging Face's sentiment analysis pipeline to evaluate financial news for risk assessment.

Apply Hugging Face's sentiment analysis pipeline with DistilBERT to evaluate financial news sentiment. Understand how fine-tuned models classify positive or negative sentiment for risk analysis.

Learn to convert sentiment analysis results into pandas DataFrames for better organization and easier visualization. Explore financial sentiment insights for multiple companies.

Learn how to use SpaCy's pre-trained model to perform Named Entity Recognition (NER). Identify companies, locations, and events in financial news to uncover valuable insights for decision-making.

Apply NER to financial news for Tesla, NIO, Rivian, and more. Convert results into readable DataFrames, enabling easy analysis of key entities like organizations and events.

Learn how to build a risk assessment function that combines sentiment analysis results and named entity recognition to calculate a company’s risk score. Understand the role of critical keywords and sentiment labels in determining risk.

Learn how to evaluate financial risk scores for multiple companies by combining sentiment analysis and named entity recognition data. Interpret the results to identify high-risk entities.

Set up your coding environment by importing essential libraries and tools. Learn to install and configure OpenAI for GPT-4, and prepare for integrating APIs like OpenWeatherMap and Google Places. ?

Learn to set up API keys for OpenWeatherMap, Google Places, and OpenAI. Understand their role in accessing weather, location, and GPT data while securely integrating them into your Colab project. ? ?

Learn to collect user inputs like destination, dates, activities, and budget using Python. These preferences will personalize your RAG++-powered travel itinerary for a unique user experience. ? ✈️

Learn to fetch real-time weather data using the OpenWeatherMap API, handle API responses, and extract meaningful insights like temperature and conditions to tailor user travel activities effectively. ? ?

Learn to fetch popular tourist spots using the Google Places API, handle activity-based searches, and extract key details like names, ratings, and addresses to enhance personalized travel plans. ?️ ?

Learn the basics of Python by understanding variables and lists. Discover how to store and organize data effectively for your programming projects.

Master the art of creating variables in Python. Learn how to assign, store, and manage data to bring your code to life.

Learn how to create and use lists in Python. Discover how to store multiple values, access them, and manipulate data efficiently.

Learn how to write if statements with multiple conditions in Python. Master combining logic to make your programs smarter and more efficient.

Learn the basics of functions in Python. Understand how to create reusable blocks of code to make your programming more efficient.

Dive deeper into Python functions. Learn how to use parameters and return values to create powerful and flexible code structures.

Understand key Python terminology. Learn the differences between scripts, modules, packages, and libraries to navigate Python programming with confidence.

Learn what a module is in Python and how it helps you organize and reuse code efficiently. Explore its role in building structured and scalable projects

Discover how to create your own Python module. Learn to structure your code for better reusability and maintainability in larger projects.

Understand why Named Entity Recognition (NER) is crucial for Question Answering systems. Learn how identifying entities like names, dates, and locations helps models provide accurate and relevant answers.

Discover why Named Entity Recognition (NER) is vital for chatbots. Learn how NER helps chatbots understand user queries better by identifying key entities like names, locations, and brands for more accurate responses.

Learn how to load a SpaCy pipeline model in this practical section. Understand the basics of initializing a model and preparing it for Named Entity Recognition (NER) tasks in real-world text analysis.

Dive into SpaCy's Named Entity Recognition (NER) attributes. Learn how to extract and interpret entity details like labels, start/end positions, and more, to gain deeper insights from text data.

Description:
Discover why learning part-of-speech tagging and syntactic parsing is crucial for understanding NLP. These skills are fundamental for building intelligent systems that understand language meaningfully.

Learn how noun phrases contribute to language structure and meaning. Understand their role in syntactic parsing and why they are crucial for building intelligent NLP models.

Understand how noun, verb, and prepositional phrases form the building blocks of sentences, and learn their significance in parsing and semantic analysis for NLP.

Learn the basics of Context-Free Grammar and how it helps define the structure of sentences, providing a foundational understanding for natural language processing.

Learn how to perform part-of-speech tagging using NLTK in Python, gaining practical skills in identifying grammatical components that form the foundation of NLP analysis.

Explore how linguistic parsers are used in real-world NLP applications. Learn how parsing helps in understanding sentence structure for chatbots, grammar checking, language translation, and information extraction.

Discover practical uses of parsers in NLP, such as information extraction, opinion mining, sentiment analysis, and question answering. Learn how parsing helps in analyzing text and extracting valuable insights.

Learn what is a Constituent, Noun Phrase and Determinant.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses GPT-4 and Python to build AI-driven applications, which is a practical way for beginners to learn NLP and machine learning

Employs libraries such as Hugging Face, NLTK, SpaCy, Keras, Sci-kit Learn, Tensorflow, and Pytorch, which are standard tools in the field

Includes a Python guide chapter for beginners, which covers fundamental basics like functions and libraries, providing a solid foundation

Explores sentiment analysis using News API to perform financial risk analysis, which is a valuable skill in the financial industry

Explores the math behind GPT, including attention, gradient loss, and Markov Models, which is essential for understanding transformer models

Requires learners to obtain API keys for services like OpenAI, OpenWeatherMap, and Google Places, which may involve additional steps and considerations

Reviews summary

Practical nlp with gpt-4 projects

According to learners, this course offers a hands-on, project-driven approach to learning Natural Language Processing and GPT-4. Students appreciate the focus on real-world applications and the use of cutting-edge tools and libraries like GPT-4, SpaCy, and Hugging Face. Many found the explanations of complex topics, such as the math behind GPT and self-attention, to be clear and accessible, even for beginners. The inclusion of Google Colab simplifies setup. However, a few reviews mention the pace can be fast for complete beginners, suggesting some prior Python knowledge is helpful.

Utilizes Google Colab, simplifying setup.

"Using Google Colab made it so easy to get started without worrying about installing libraries locally. Setup was hassle-free."

"I appreciated the emphasis on Colab. It kept the focus on the code and concepts rather than environment issues."

"No tedious Anaconda or Jupyter installs required, which was a big time-saver."

"Learned how to efficiently use Colab notebooks for my Python and NLP projects."

Explains complex math and theory accessibly.

"The way the math behind GPT and attention mechanisms was explained using simple analogies was brilliant. It made complex topics easy to grasp."

"Even though I'm new to this, the instructor broke down concepts like word embeddings and cosine similarity in a very understandable way."

"I was intimidated by the math, but the course makes it manageable with clear visuals and step-by-step guidance."

"Really helped solidify my understanding of attention and transformer concepts with the engaging explanations."

Features up-to-date content on GPT-4 and modern NLP.

"The course is very current, covering GPT-4, RAG, and modern libraries like Hugging Face. This is exactly what I needed to stay relevant in the field."

"I appreciated learning how to use the OpenAI API with Python for practical tasks. The focus on GPT-4 is a major plus."

"Getting hands-on with SpaCy and sentiment analysis on real financial data felt very practical and applicable to current trends."

"Explores cutting-edge technologies like GPT-4 and Retrieval-Augmented Generation (RAG++)."

Course is strongly focused on practical projects.

"The hands-on coding and projects are the strongest part of the course for me, really helping solidify concepts with practical application."

"I loved building the real-world projects, especially the chatbot and recipe generator. It's the best way to learn by doing practical tasks."

"The project-based structure is fantastic. It made learning NLP concepts much more engaging and I have great additions to my portfolio."

"Working through the projects gave me the confidence to start my own NLP applications."

May be fast for absolute beginners; Python helps.

"While it says 'for beginners', I found the pace quite fast in places, especially if you're not already comfortable with Python."

"Some sections felt a bit rushed. I recommend having a solid grasp of Python basics before starting."

"As a complete beginner, I had to pause and rewatch some lectures multiple times to keep up. It requires significant focus."

"Could benefit from more foundational Python material or a clearer statement of prerequisites."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Complete NLP & GPT-4 Course: Real-World Python Projects with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python basics, including variables, data structures, and control flow, to prepare for the Python-heavy projects in this course.

Browse courses on Python Basics

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Review Python syntax and data types.
Practice writing simple Python scripts.
Complete online Python tutorials.

Brush Up on NLP Fundamentals

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Revisit core NLP concepts like tokenization, stemming, and part-of-speech tagging to build a solid foundation for the advanced topics covered in the course.

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Review NLP concepts.
Read introductory NLP articles.
Watch introductory NLP videos.

Read 'Natural Language Processing with Python'

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Gain a deeper understanding of NLP fundamentals and NLTK library, which provides a strong base for understanding advanced concepts.

View Natural Language Processing with Python:... on Amazon

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Read the introductory chapters on NLP concepts.
Work through the examples using NLTK.
Summarize key concepts from each chapter.

Four other activities

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Build a Simple Sentiment Analyzer

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Apply your NLP knowledge by building a sentiment analyzer using a pre-trained model, reinforcing your understanding of sentiment analysis and model integration.

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Choose a dataset of text with sentiment labels.
Load a pre-trained sentiment analysis model.
Evaluate the model on your dataset.
Refine the model based on the results.

Create a Blog Post on GPT-4 Applications

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Solidify your understanding of GPT-4 by researching and writing a blog post about its various applications, showcasing your knowledge and communication skills.

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Research different applications of GPT-4.
Choose a specific application to focus on.
Write a detailed blog post explaining the application.
Edit and publish your blog post.

Read 'Hugging Face Transformers'

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Deepen your knowledge of transformer models and the Hugging Face library, enhancing your ability to build advanced NLP applications.

View Putting Knowledge to Work on Amazon

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Read the chapters on transformer architectures.
Experiment with different transformer models using Hugging Face.
Implement a project using a transformer model.

Contribute to an NLP Open Source Project

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Enhance your skills and contribute to the NLP community by contributing to an open-source project, gaining practical experience and collaborating with other developers.

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Find an NLP open-source project on GitHub.
Identify an issue or feature to work on.
Submit a pull request with your changes.
Respond to feedback and refine your contribution.

Career center

Learners who complete The Complete NLP & GPT-4 Course: Real-World Python Projects will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

A natural language processing engineer builds systems that allow computers to understand, interpret, and generate human language. This course helps build a strong foundation in NLP, covering key concepts like tokenization, embeddings, and attention mechanisms, which are essential for this role. The course's hands-on projects, such as building chatbots and performing sentiment analysis, offer practical experience directly applicable to the work of an NLP engineer, helping you to develop real world applications. A person who wishes to enter the field should especially take note of this course's coverage of transformer models such as GPT-4, as well as the math behind them.

See salaries and explore the career path for Natural Language Processing Engineer

AI Chatbot Developer

An AI chatbot developer creates conversational agents that interact with users through text or voice. This course is ideal because it provides hands-on experience with building a custom chatbot for small businesses, using the OpenAI API and GPT-4, a major skillset for the role. You'll learn to apply NLP techniques for natural and engaging conversations. The inclusion of projects that use scraped data to train chatbots, such as the online bookstore chatbot, will provide a practical understanding of the workflow. This course is particularly useful because it allows you to practice using the tools needed for the job.

See salaries and explore the career path for AI Chatbot Developer

AI Application Developer

An AI application developer designs and builds AI-powered applications that solve real-world problems. The skills taught in this course, such as utilizing the OpenAI API and working with transformer models like GPT-4, are directly applicable to creating cutting-edge AI applications. The course's hands-on projects, like the recipe generator and marketing content generator, provide experience developing practical tools using AI. This course offers valuable experience for those who want to build AI-powered solutions. This course is particularly relevant for those who wish to enter this field.

See salaries and explore the career path for AI Application Developer

Machine Learning Engineer

A machine learning engineer develops and implements machine learning models and algorithms. This course provides an introduction to the fundamental concepts of NLP, including practical applications like sentiment analysis and named entity recognition, which are increasingly important in the field of machine learning. This course also covers transformer models and the math behind them, an important component of machine learning. This course will help you to build a strong foundation to apply machine learning to real-world problems, preparing them for advanced roles in the field. This is an ideal course for aspiring machine learning engineers.

See salaries and explore the career path for Machine Learning Engineer

Content Generation Specialist

A content generation specialist creates varied types of content, often using AI tools to improve efficiency and creativity. This course is especially useful because it teaches you how to develop a custom marketing content generator using GPT-4, giving real-world experience with content creation. The course project on interactive storytelling also hones your skills in creating compelling narratives. The course also covers more than just text, showing how AI can be used to generate creative recipes, and therefore may be useful for all sorts of content creators. A person wishing to enter this field would benefit from this course.

See salaries and explore the career path for Content Generation Specialist

Data Scientist

A data scientist uses statistical methods and machine learning algorithms to analyze data, extract insights, and drive decision-making. The course helps build a foundation in NLP and provides practical skills to analyze text data, an increasingly important aspect of data science. This course covers techniques such as sentiment analysis, named entity recognition, and topic modeling, which are valuable for extracting insights from textual datasets. The ability to perform financial risk analysis using sentiment analysis, as taught in this course, may also be useful for a data scientist working in the financial domain. This course may be useful for data scientists.

See salaries and explore the career path for Data Scientist

Computational Linguist

A computational linguist develops algorithms and models to process and understand human language. This course introduces key NLP concepts like tokenization, embeddings, and attention mechanisms, building a good foundation for this career. The understanding of the math behind GPT, including topics such as self-attention, and the practical application of SpaCy for named entity recognition, will help prepare you for the kind of work done in this field. Aspiring computational linguists should find this course useful for entering the field.

See salaries and explore the career path for Computational Linguist

Financial Analyst

A financial analyst examines financial data to provide insights and recommendations to businesses. The course provides a project on performing financial risk analysis using sentiment analysis on news articles, which is directly relevant to the financial industry. You will learn to apply sentiment analysis and named entity recognition (NER), all of which may be useful in assessing market trends. A person entering this field may find this course helpful, especially its focus on practical applications.

See salaries and explore the career path for Financial Analyst

Marketing Automation Specialist

A marketing automation specialist implements and manages marketing automation strategies and tools, with the goal of improving efficiency and campaign effectiveness. The course teaches you how to create personalized marketing campaigns using Python and GPT-4, which directly applies to this role. You will also learn how to tailor messages for specific audiences, enhancing your ability to create effective marketing strategies. The focus on practical projects, which provide hands-on experience, makes this course particularly beneficial for aspiring marketing automation specialists. This course may be useful to prospective marketing automation specialists, especially if they're looking for hands-on experience.

See salaries and explore the career path for Marketing Automation Specialist

Research Scientist

A research scientist conducts advanced research in various fields, often contributing to scientific literature, and making new discoveries in their field. This course will help you develop a fundamental understanding of NLP techniques, particularly in the context of transformer models. The course also provides hands-on experience, which may assist you in conducting research in NLP or AI-related topics. The course's content on the math behind models like GPT may be useful. A person who plans to go into research should see if the course fits their specific needs.

See salaries and explore the career path for Research Scientist

Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends, patterns, and insights that can help a company make better decisions. While this course does not focus on data analysis in general, the skills learned, such as performing sentiment analysis and named entity recognition on text data, may be useful in this role. The course's project on financial risk analysis and using data scraping techniques to gather datasets, may offer a different perspective to business intelligence. This course may be useful for certain business intelligence analysts, particularly those who wish to analyze textual data.

See salaries and explore the career path for Business Intelligence Analyst

Market Research Analyst

A market research analyst studies market conditions to assess the potential sales of a product or service. The ability to perform sentiment analysis on news articles, as taught in this course, may be useful to gauge market sentiment towards a particular product or company. You will also learn how to use named entity recognition, which can be valuable in identifying key players and trends in the market. This course may be helpful for market research analysts who seek to expand their skill set with NLP techniques, but is not primarily focused on their field.

See salaries and explore the career path for Market Research Analyst

Technical Writer

A technical writer creates documentation, manuals, and guides for technical products and services, often in the tech industry. This course may be useful because they require a strong understanding of technology and how it works. The course project on interactive storytelling can help you develop skills in explaining complex topics in an engaging way, while the course's general hands-on nature will give you a broader understanding of technology. A prospective technical writer should consider whether this course fits in with their professional development goals.

See salaries and explore the career path for Technical Writer

Product Manager

A product manager oversees the development and strategy of a product, and this requires a deep understanding of the technology and user needs. While this course doesn't directly teach product management, the understanding of AI concepts, particularly in the realm of NLP, may help you to manage a product that uses this technology. You'll be able to develop a familiarity with the capabilities of AI which may help in your communication with engineers. This course may be beneficial to a product manager who wants to gain understanding of the technology behind the products they manage.

See salaries and explore the career path for Product Manager

Project Manager

A project manager plans, executes, and closes projects, often in a tech-driven environment, with focus on completing projects on time. The practical, hands-on nature of this course may be useful for understanding the kind of work that goes into an AI-related project. The course’s several projects, each covering a single type of application, will give you a good understanding of the project lifecycle for those projects. The course is not designed for project management in particular, but the familiarity gained may help you manage AI-related projects more effectively. This course may be useful.

See salaries and explore the career path for Project Manager

The Complete NLP & GPT-4 Course

Real-World Python Projects

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What's inside

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Syllabus

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Reviews summary

Practical nlp with gpt-4 projects

Activities

Career center

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