# Machine Learning: Natural Language Processing in Python (V2)

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## NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python

**What you’ll learn in Machine Learning: Natural Language Processing in Python
**

- How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe
- How to implement a document retrieval system / search engine / similarity search / vector similarity
- Probability models, language models and Markov models (prerequisite for Transformers, BERT, and GPT-3)
- How to implement a cipher decryption algorithm using genetic algorithms and language modeling
- How to implement spam detection
- How to implement sentiment analysis
- How to implement an article spinner
- How to implement text summarization
- How to implement latent semantic indexing
- How to implement topic modeling with LDA, NMF, and SVD
- Machine learning (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
- Deep learning (ANNs, CNNs, RNNs, LSTM, GRU) (more important prerequisites for BERT and GPT-3)
- Hugging Face Transformers (VIP only)
- How to use Python, Scikit-Learn, Tensorflow, +More for NLP
- Text preprocessing, tokenization, stopwords, lemmatization, and stemming
- Parts-of-speech (POS) tagging and named entity recognition (NER)

**Requirements**

- Install Python, it’s free!
- Decent Python programming skills
- Optional: If you want to understand the math parts, linear algebra and probability are helpful

#### Description

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 methods

2) Probability models and Markov models

3) Machine learning methods in Machine Learning: Natural Language Processing in Python

4) 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:

- 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:

- Naive Bayes
- Logistic Regression
- Principal Components Analysis (PCA) / Singular Value Decomposition (SVD)
- Latent Dirichlet Allocation (LDA)

These are not just “any” machine learning / artificial intelligence algorithms but rather, ones that have been staples in NLP and are thus an essential part of any NLP course.

In part 4, which covers *deep learning methods*, you’ll learn about modern neural network architectures that can be applied to solve NLP tasks. Thanks to their great power and flexibility, neural networks can be used to solve any of the aforementioned tasks in the course.

You’ll learn about:

- Feedforward Artificial Neural Networks (ANNs)
- Embeddings
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)

The study of RNNs will involve modern architectures such as the LSTM and GRU which have been widely used by Google, Amazon, Apple, Facebook, etc. for difficult tasks such as language translation, speech recognition, and text-to-speech.

Obviously, as the latest **Transformers** (such as **BERT **and **GPT-3**) are examples of deep neural networks, this part of the course is an essential prerequisite for understanding Transformers.

Thank you for reading and I hope to see you soon!

#### Who this course is for:

- Anyone who wants to learn natural language processing (NLP)
- Anyone interested in artificial intelligence, machine learning, deep learning, or data science
- Anyone who wants to go beyond typical beginner-only courses on Udemy

**Created by Lazy Programmer Inc., Lazy Programmer Team**

**Size: 2.89GB**

**https://www.udemy.com/course/natural-language-processing-in-python/**

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