Lazy Programmer Inc.
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.
Instructor Details
Courses : 22
Specification: Data Science: Natural Language Processing (NLP) in Python
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14 reviews for Data Science: Natural Language Processing (NLP) in Python
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| Price | $12.99 |
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| Provider | |
| Duration | 10 hours |
| Year | 2020 |
| Level | All |
| Language | English ... |
| Certificate | Yes |
| Quizzes | No |
$12.99
Giovanni Salvi –
So far, I’m really impressed about the quality and level of the course!
Devatha Manasa –
can you explain with more examples.
Devansh Srivastava –
Great for beginners to know about nlp with machine learning and regular expression. Looking forward to lazy programmer’s nlp course with deep learning!
Dominic Awa –
Straightforward course without any unnecessary deviations. Pacing is perfect most especially for viewers with prior knowledge in Machine Learning and user interface of sklearn.
Hui Xu –
The goal and scope of this course is clear and concise.
M. Hidayat Budi Kusumo –
apiiiiik tenan…. iki kursuse juancuk….
Tianxiang Zhou –
I finished almost all the lectures. Section 3 is a complete disaster but all the other sections are pretty good. Section 3 talked about a weird modified genetic algorithm which claimed to have some analogies with the language model. I felt like a complete idiot after taking lecture 9 to 11 and I had no clue how this is related. I feel like this section is unclear and the instructor shouldn’t talk about useless information about biology.
Salome Bangera –
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Rahul Vimal –
Course was good but the data pre processing was missing. As the Instructor rightly said that data pre processing is a critical part in any modelling and since most of the real life data sets are unclean, Data Pre processing of Text’s/ emails can be included in the course to give a wholistic view.
Manish Kapoor –
Good till now
Jo Olu Jose –
It’s a good match, I’m currently an analytic manager, trying to incorporate NLP into current activities at work
Sureshkumar Maddala –
I cannot understand the analogy with DNA
Rajesh Kumar Aggarwal –
Coding part can be improved
Bowen Chen –
Simply amazing. Great foundational course to NLP. Just make sure you follow his instruction and try coding yourself