Introduction to the Course

Welcome to the exciting universe of Natural Language Processing (NLP)—where words meet algorithms, and machines learn to understand human language. Let’s explore how you can wield this power, especially with the OpenAI API with Python.

What is natural language processing?

Natural language processing (NLP) is a branch of artificial intelligence (AI) that deals with computers using AI to generate, comprehend, and manipulate human language. NLP is the backbone of tools like Google Translate, Siri, Alexa, and chatbots. It allows computers to understand, generate, and manipulate human language.

Consumers rely increasingly on search engines, technical software programs, and virtual assistants to answer their questions, so the demand for effective and scalable NLP solutions has increased immensely. This course will teach you how to use the OpenAI API with Python to interact with state-of-the-art NLP models, empowering you to tackle various language tasks such as summarization, classification, text completion, and more.

In this course, you’ll learn about the various endpoints of the OpenAI API and how they can be used to accomplish certain NLP tasks.

How does natural language processing work? 

At its core, NLP teaches machines to understand and generate human language. Achieving this involves a mix of linguistic rules and machine learning algorithms

Step 1: Breaking the text into pieces

When you type or speak, NLP starts by dividing your input into smaller components:

• Tokenization: Splitting text into words or phrases (e.g., “I love NLP” → [“I,” “love,” “NLP”]).

• Sentence segmentation: Breaking paragraphs into sentences.

Step 2: Understanding the words

Once the text is split, the system needs to figure out what the words mean:

• Word sense disambiguation: Determining the correct meaning of a word based on context. For instance, “bank” can mean a riverbank or a financial institution.

• Part-of-Speech tagging: Identifying if a word is a noun, verb, adjective, etc.

Step 3: Finding the bigger picture

Words alone don’t tell the full story, so the system looks at the relationships between them:

• Named entity recognition (NER): Identifying proper names (e.g., “Paris” as a city, “Elon Musk” as a person).

• Parsing: Figuring out how words relate grammatically to one another.

Step 4: Applying artificial intelligence

Now comes the AI magic:

• Sentiment analysis: Detecting the tone or emotion (e.g., happy, sad, neutral).

• Summarization: Condensing information into shorter, meaningful text.

• Question answering: Responding to user queries by analyzing the input and retrieving answers.

Step 5: Continuous Learning

Modern NLP models like OpenAI’s GPT continuously improve through:

• Training on massive data: Learning from billions of text samples.

• Feedback: Using user interactions to refine responses.

Understanding these steps gives you an appreciation for the complexity behind tools like Siri, Google Translate, and chatbots. Each step is like a piece of a puzzle that comes together to make human-computer communication seamless and effective.

What is an API?

An application programming interface (API) is a software gateway that allows different software components to communicate with each other. An API helps expose an application's capabilities to the outside world, allowing programmatic access to its data.

Consider the case of an application that allows performing natural language processing tasks on the provided input. Building and exposing an API for this application will allow others to programmatically perform NLP operations such as classification and summarization on the provided text.

Prerequisites

This course assumes that readers have the following:

• A basic understanding of APIs

• A basic knowledge of Python

• A basic knowledge of ML and NLP concepts

Intended audience

This course is intended for the following types of learners:

• A beginner who wants to start with machine learning (ML) APIs.

• A learner who has some knowledge of different NLP tasks and wants to explore the OpenAI API for NLP tasks

• A professional who has already worked with different NLP models and wants to integrate the OpenAI API into their project

Course structure

The course would explore three endpoints: completions, moderations, and embeddings. Each endpoint is used for different purposes. For each endpoint, we would look at various examples and tasks that can be achieved through them and their real-world use cases. We would then create a demo application to observe how to integrate one of these endpoints into our applications.

Take a look at the mind map of the course below.