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Mitch Miller – Lazy Consultant System

WEBRip | English | MP4 + MP3 | 852 x 440 | AVC ~596 Kbps | 30 fpsAAC | 126 Kbps | 44.1 KHz | 2 channels | 06:25:36 | 2.27 GB Genre: Video Tutorial / Business, Sales, Marketing
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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:

• Building a text classifier
• Article spinning
• Text generation (generating poetry)

Importantly, these methods are an essential prerequisite for understanding how the latest Transformer (attention) models such as BERT and GPT-3 work. Specifically, we’ll learn about 2 important tasks which correspond with the pre-training objectives for BERT and GPT.

In part 3, which covers machine learning methods, you’ll learn about more of the classic NLP tasks, such as:

• Spam detection
• Sentiment analysis
• Latent semantic analysis (also known as latent semantic indexing)
• Topic modeling

This section will be application-focused rather than theory-focused, meaning that instead of spending most of our effort learning about the details of various ML algorithms, you’ll be focusing on how they can be applied to the above tasks.

Of course, you’ll still need to learn something about those algorithms in order to understand what’s going on. The following algorithms will be used: