[2026] Machine Learning: Natural Language Processing (V2)

About this course

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

Hello friends!

Welcome to Machine Learning: Natural Language Processing in Python (Version 2).

This is a massive 4-in-1 course covering:1) Vector models and text preprocessing methods2) Probability models and Markov models3) Machine learning methods4) Deep learning and neural network methods

In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe.

You'll then apply what you learned for various tasks, such as:

• Text classification
• Document retrieval / search engine
• Text summarization
• Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization.
• You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.

In part 2, which covers probability models and Markov models, you'll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning.

In this course, you'll see how such probability models can be used in various ways, such as:

• Buildin

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.