AI Cheat Sheet 2: Natural Language Processing (NLP)

Check out the AI Cheat Sheet 1: AI Fundamentals here.

The world’s on fast-forward thanks to AI, and who wouldn’t feel a little pressure to keep up? We chug down articles, podcasts, and videos to stay in the loop. But keeping up with AI feels like racing against time — we want to learn, improve, and make progress. We spend hours reading, listening, and watching to soak in all the smart stuff. But, here’s the tricky part: when we need that knowledge, it often plays hide and seek in our brains because our brains can’t store too many thoughts at once.

That’s where the “AI Cheat Sheet” blog post series swoops in like a superhero! Think of it as your go-to guide, a brain boost in a colorful, picture-packed format that makes those tricky AI concepts stick like glue. No more struggling to remember what an algorithm is or why robots are learning to write poetry (seriously!).

So, the next time your brain feels like a sieve for AI knowledge, don’t panic! Just head over to this “AI Cheat Sheet” and give your memory a juicy boost. You’ll be talking AI lingo like a pro in no time!

Remember, keeping up with AI shouldn’t be a chore. We’re here to make it fun, easy, and oh-so-memorable. Now go forth and conquer the world of AI, one colorful cheat sheet at a time!

Let’s get started.

What is NLP?

Where is NLP used?

NLP Pipeline

Preprocessing refers to the series of steps taken to clean and transform raw data before it is used for natural language processing (NLP) tasks.

Feature extraction is responsible for extracting clues or keywords from the text.

Imagine you have a big pile of toys. There are cars, trucks, dolls, blocks, and all sorts of other things. How would you find a specific toy, like a red fire truck? You could look through all the toys one by one, but that would take a long time.

For details on Feature Extraction Techniques, refer here.

Modeling is the final step in the NLP pipeline, where we create a statistical or machine learning model. We design this model to learn from training data and make predictions about new, unseen data.

Once you have a working model, you can deploy it as a web app, mobile app, or integrate it with other products and services. The possibilities are endless!

AI Cheat Sheet 3: LLM Fundamentals