In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so there are no mathematical prerequisites - just straight up coding in Python. All the materials for this course are FREE.

After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a**cipher decryption algorithm**. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, **character-level language models (using the Markov principle)**, and **genetic algorithms**.

The second project, where we begin to use more traditional "**machine learning**", is to build a **spam detector**. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we'll build a model for**sentiment analysis** in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to **predict the stock market**.

We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

Finally, we end the course by building an**article spinner**. This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them!

This course focuses on**"how to build and understand"**, not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about **"seeing for yourself" via experimentation**. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

Suggested Prerequisites:

After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a

The second project, where we begin to use more traditional "

Next we'll build a model for

We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

Finally, we end the course by building an

This course focuses on

Suggested Prerequisites:

- calculus
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- Sci-Kit Learn API, working knowledge of machine learning
- Some familiarity with PCA, Markov Models, Logistic Regression

- Introduction and Outline (03:04) (FREE preview available)
- NLP Applications (06:40)
- Why is NLP hard? (03:59)
- The Central Message of this Course (02:22)

- How to Succeed in this Course (03:13)
- Where to get the code and data (02:42)
- Do you need a review of machine learning? (02:45)

- Section Introduction (04:50)
- Ciphers (03:59)
- Language Models (16:07)
- Genetic Algorithms (21:23)
- Code Preparation (04:46)
- Code pt 1 (Notebook in Extras Section) (03:07)
- Code pt 2 (07:20)
- Code pt 3 (04:52)
- Code pt 4 (04:03)
- Code pt 5 (07:12)
- Code pt 6 (05:25)
- Section Conclusion (06:00)

- Build your own spam detector - description of data (02:08)
- Build your own spam detector using Naive Bayes and AdaBoost - the code (06:16)
- Key Takeaway from Spam Detection Exercise (05:56)
- Naive Bayes Concepts (09:56)
- AdaBoost Concepts (05:11)
- Other types of features (01:30)
- Spam Detection FAQ (Remedial #1) (08:45)
- What is a Vector? (Remedial #2) (06:04)
- SMS Spam Example (06:23)
- SMS Spam in Code (10:43)

- Description of Sentiment Analyzer (03:12)
- Logistic Regression Review (07:32)
- Preprocessing: Tokenization (04:48)
- Preprocessing: Tokens to Vectors (06:20)
- Sentiment Analysis in Python using Logistic Regression (19:48)
- Sentiment Analysis Extension (06:01)
- How to Improve Sentiment Analysis & FAQ (12:19)

- NLTK Exploration: POS Tagging (02:00)
- NLTK Exploration: Stemming and Lemmatization (02:06)
- NLTK Exploration: Named Entity Recognition (03:13)
- Want more NLTK? (01:59)

- Latent Semantic Analysis - What does it do? (02:30)
- PCA and SVD - The underlying math behind LSA (15:49)
- Latent Semantic Analysis in Python (10:08)
- What is Latent Semantic Analysis Used For? (09:40)
- Extending LSA (06:16)

- Article Spinning Introduction and Markov Models (02:43)
- Trigram Model (02:12)
- More about Language Models (09:53)
- Precode Exercises (05:05)
- Writing an article spinner in Python (11:33)
- Article Spinner Extension Exercises (05:42)

- What we didn't talk about (02:45)

- Machine Learning: Section Introduction (07:47)
- What is Classification? (12:22)
- Classification in Code (14:38)
- What is Regression? (12:13)
- Regression in Code (08:29)
- What is a Feature Vector (06:48)
- Machine Learning is Nothing but Geometry (04:50)
- All Data is the Same (05:23)
- Comparing Different Machine Learning Models (09:46)
- Machine Learning and Deep Learning: Future Topics (05:55)
- Section Summary (05:47)

- What is the Appendix? (02:48)
- Windows-Focused Environment Setup 2018 (20:20)
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
- How to Code Yourself (part 1) (15:54)
- How to Code Yourself (part 2) (09:23)
- Proof that using Jupyter Notebook is the same as not using it (12:29)
- What order should I take your courses in? (part 1) (11:18)
- What order should I take your courses in? (part 2) (16:07)
- Python 2 vs Python 3 (04:38)
- How to Succeed in this Course (Long Version) (10:24)
- Where to get discount coupons and FREE deep learning material (05:31)

- Cipher Decryption Colab Notebook