Data Science: Natural Language Processing (NLP) in Python
Original price was: $109.99.$19.99Current price is: $19.99.
What you’ll learn
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Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models
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Write your own spam detection code in Python
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Write your own sentiment analysis code in Python
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Perform latent semantic analysis or latent semantic indexing in Python
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Have an idea of how to write your own article spinner in Python
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 it doesn’t contain any hard math – 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.
“If you can’t implement it, you don’t understand it”
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Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
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My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
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Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
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After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…
Suggested Prerequisites:
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Python coding: if/else, loops, lists, dicts, sets
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Take my free Numpy prerequisites course (it’s FREE, no excuses!) to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics
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Optional: If you want to understand the math parts, linear algebra and probability are helpful
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
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Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
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.
12 reviews for Data Science: Natural Language Processing (NLP) in Python
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Original price was: $109.99.$19.99Current price is: $19.99.
Lyndon Castro –
Rude instructor refused to answer a simple question appropriately. TERRIBLE instructor and company. I and my companies will NEVER do business with these disgraceful people.
David Hood –
I found the style of delivery to be poor, with the lecturer both sarcastic and patronising of lower abilities – frequently launching off into tangents to justify his attitudes and style of delivery. I would advise this course is reviewed for actual quality of content.
Marshal E Wigwe –
The course was very informative and the instructor is very knowledgeable in the content and the delivery was good too.
Cham Kao –
Very useful techniques discussed. Though the cipher decryption is a bit complex but the rest are very useful and can be practiced on business datasets.
Sahil Batra –
There must be some beginners on this site who get insulted by the instructor saying such things like how to succeed and please do some coding, but after reading their comments I think it is justified. When reading in this context it becomes quite funny. The instructor always has a good sense of humor and the lectures have this fun style. I can understand if you are a beginner making all the mistakes he is saying, you might feel demotivated. To me, it’s apt, since I don’t want to learn a beginners style course. The beginners should try to live up to the expectations set by the course prerequisites.
Marcos Chaves Martins –
For me it suited me well on my objective of gaining a basic understanding of NLP, Data Science and Deep Learning. We have Q&A answered promplty, which is really awesome. Also, the instructor is very transparent on his objectives, what you are getting in this course and where you should go for covering gaps or extending the knowledge.
Julia Holt –
Fantastic! Just what I needed to get started on my own project. I was impressed by the instructor’s fluency in the subject and breadth of coverage. He speaks confidently and doesn’t waste any words beating around the bush.
Peter Waldmann –
This is a very well put together and comprehensive NLP course. I feel it goes a bit beyond the beginner level. As with most courses by the Lazy Programmer, he gives a lot of mathematical detail. It was an engaging course to listen to.
Suneel Reddy –
Not really helpful to be honest. Everything is rushed and no explanation what so ever. I do understand the pre-requisites and I totally fit those, but still couldn’t follow most of the things. Its more like ‘Let me finish this’ than explain things. I do get the context of each topic, but wanted to understand the contents clearly – not on high level.
Ravi Sikka –
It was a good course and helped me a lot in my studies. Thank you for explaining in detail that despite being a newbie, I was able to get it without any difficulty.
Jaymin Desai –
Excellent starting point to learn NLP. Good material and simple teaching which help to grasp the content easily and fast. Code examples are kept up to date by instructor.
James Kozak –
With all of the NLP tutorials out there, this is by far the best introduction. NLP is a wide field that needs a lot of background about various topics like machine learning and linguistics but this helps condense it into the bare-bones of how to use NLP effectively.