Who this course is for:
- Students who are comfortable writing Python code, using loops, lists, dictionaries, etc.
- Students who want to learn more about machine learning but don’t want to do a lot of math
- Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis
- This course is NOT for those who find the tasks and methods listed in the curriculum too basic.
- This course is NOT for those who don’t already have a basic understanding of machine learning and Python coding (but you can learn these from my FREE Numpy course).
- This course is NOT for those who don’t know (given the section titles) what the purpose of each task is. E.g. if you don’t know what “spam detection” might be useful for, you are too far behind to take this course.
Course content
14 sections • 77 lectures • 9h 44m total length
Natural Language Processing – What is it used for?
3 lectures • 22min
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Introduction and Outline Preview07:48
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Why Learn NLP? 05:59
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The Central Message of this Course (Big Picture Perspective) 08:12
Course Preparation
4 lectures • 13min
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How to Succeed in this Course 05:18
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Where to get the code and data 02:42
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Do you need a review of machine learning? 02:45
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How to Open Files for Windows Users 02:18
Decrypting Ciphers
13 lectures • 1hr 29min
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Section Introduction 04:50
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Ciphers 03:59
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Language Models 16:06
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Genetic Algorithms 21:23
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Code Preparation 04:46
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Link to Cipher Notebook 00:00
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Code pt 1 03:06
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Code pt 2 07:20
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Code pt 3 04:52
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Code pt 4 04:03
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Code pt 5 07:12
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Code pt 6 05:25
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Section Conclusion 06:00
Build your own spam detector
11 lectures • 1hr 5min
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Build your own spam detector – description of data 02:08
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Build your own spam detector using Naive Bayes and AdaBoost – the code 05:14
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Key Takeaway from Spam Detection Exercise 05:56
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Naive Bayes Concepts 09:56
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AdaBoost Concepts 05:11
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Other types of features 01:30
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Spam Detection FAQ (Remedial #1) 08:45
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What is a Vector? (Remedial #2) 06:04
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SMS Spam Example 06:23
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SMS Spam in Code 10:43
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Suggestion Box 03:03
Build your own sentiment analyzer
7 lectures • 1hr
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Description of Sentiment Analyzer 03:12
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Logistic Regression Review 07:32
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Preprocessing: Tokenization 04:48
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Preprocessing: Tokens to Vectors 06:20
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Sentiment Analysis in Python using Logistic Regression 19:48
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Sentiment Analysis Extension 06:01
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How to Improve Sentiment Analysis & FAQ 12:19
NLTK Exploration
4 lectures • 9min
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NLTK Exploration: POS Tagging 02:00
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NLTK Exploration: Stemming and Lemmatization 02:06
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NLTK Exploration: Named Entity Recognition 03:13
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Want more NLTK? 01:59
Latent Semantic Analysis
5 lectures • 44min
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Latent Semantic Analysis – What does it do? 02:30
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SVD – The underlying math behind LSA 15:49
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Latent Semantic Analysis in Python 10:08
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What is Latent Semantic Analysis Used For? 09:40
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Extending LSA 06:16
Write your own article spinner
6 lectures • 37min
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Article Spinning Introduction and Markov Models 02:43
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Trigram Model 02:11
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More about Language Models 09:53
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Precode Exercises 05:05
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Writing an article spinner in Python 11:33
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Article Spinner Extension Exercises 05:42
How to learn more about NLP
1 lecture • 3min
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What we didn’t talk about 02:45
Machine Learning Basics Review
11 lectures • 1hr 34min
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(Review) Machine Learning: Section Introduction: 07:47
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(Review) What is Classification? 12:22
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(Review) Classification in Code 14:38
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(Review) What is Regression? 12:13
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(Review) Regression in Code 08:29
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(Review) What is a Feature Vector? 06:48
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(Review) Machine Learning is Nothing but Geometry 04:50
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(Review) All Data is the Same 05:23
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(Review) Comparing Different Machine Learning Models 09:46
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(Review) Machine Learning and Deep Learning: Future Topics 05:55
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(Review) Section Summary 05:47
Setting Up Your Environment
2 lectures • 38min
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Windows-Focused Environment Setup 2018 20:20
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How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32
Extra Help With Python Coding for Beginners
4 lectures • 42min
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How to Code by Yourself (part 1) 15:54
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How to Code by Yourself (part 2) 09:23
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Proof that using Jupyter Notebook is the same as not using it 12:29
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Python 2 vs Python 3 04:38
Effective Learning Strategies for Machine Learning
4 lectures • 1hr
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How to Succeed in this Course (Long Version) 10:24
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Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04
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What order should I take your courses in? (part 1) 11:18
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What order should I take your courses in? (part 2) 16:07
