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Data Science

In this course you will build 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.

Suggested Prerequisites:

  • Python coding: if/else, loops, lists, dicts, sets

  • 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

  • Optional: If you want to understand the math parts, linear algebra and probability are helpful

TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Ask lots of questions on the discussion board. The more the better.

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

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Rating 4.1 based on 505 ratings
Length 10 total hours
Starts On Demand (Start anytime)
Cost $12
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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What people are saying

machine learning

Good course, not very basic, but still basic... you need some understand about Machine Learning.

If I have a little experience of machine learning, it's more through its application to computer vision.

Good for a complete begginer who has just heard about NLP/Machine learning and basic python understanding.

I liked the lectures on machine learning which provided a good review.

The machine learning review was so useful.

Covers a nice amount of topics in NLP and machine learning.

I recommend the instructor to go to the Machine Learning course by Kirill Eremenko on Udemy for potential improvement(s).

Read more

lazy programmer

As lazy programmer says "All data is same" and "ML is just geometry and nothing more".

Lazy programmer does a superb job of engaging the student throughout the course.

All the lazy programmer courses are done very well, they give complete preparation but they are very challenging; their sequence forms a pleasant and complete course path; congratulations to the author.

Lazy Programmer very clearly explains each model and the pros and cons of each one.

I am thankful to the Lazy Programmer for designing a course that introduces NLP to the beginner.

Lazy programmer is a superb teacher and explains each concept in simple ways for everyone to understand.

Lazy Programmer is really unique and presents the topic in a very appealing way.

Read more

sentiment analysis

For instance, considerable portion of the course is spent to convey the massage that "all data is the same" and that working with text "is nothing special", but then in the simplest assignment (sentiment analysis) we suddenly see that you have to preprocess your text data by cleaning it (e.g.

The fact is, however, if you were to make your own sentiment analysis application, you would have no idea how to do it.

This was notable especially in the sentiment analysis section.

I found the sentiment analysis example was great.

The sentiment analysis exercise shows how to interpret the results of training the model.

The point of the section 11 "How to improve your sentiment analysis" is not to introduce RNN, but show how student are stupid.

It is great, you will learn sentiment analysis and advanced text transformation model.

Read more

natural language processing

This course helped to establish how natural language processing is related to machine learning.

This was a good introduction to natural language processing.

A nice start to natural language processing.

Content was solid and covered practical applications of natural language processing in Python.

Simple and to the point course on natural language processing.

Was expecting more Natural Language Processing, this seemed more about the next stage which was applying Machine Learning concepts to the already processed data.

Compelling and perfect introduction to the domain of natural language processing Good content and explanation.

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article spinning

I am happy to learn LSA and Article Spinning topics from this course.

The article spinning section shows how to build a customized algorithm in python.

Direct and to the point examples like sentiment analysis and article spinning.

fantastic course, learned so much from the article spinning practical fast-paced course with exciting realistic projects these are great ready-to-use examples Very clear and precise The course is exactly, what I was looking for.

Read more

easy to follow

Practical examples that are easy to follow goodintroduction.nowiwanttoseecode.

The course was well planned and easy to follow.

Code is easy to follow and modify for personal application.

Very easy to follow and code along to get a practical sense of how everything is working.

This is a good first course on NLP with easy to follow and straightforward examples.

The instructor explains things step-by-step so it's easy to follow.

concise and well documented This course gives a pretty good overview of NLP Easy to follow and merges and advanced concepts Useful course projects with hands on experience Excellent for base concepts in NLP in python.

Read more

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Well analyst $76k

Well Operator Consultant $78k

Well Test / Admin $85k

IT Build Coordinator $86k

Well Planner 2 $88k

Well Tester 3 $88k

Well Engineer $93k

Well Operator $94k

Well Tie-In Designer $102k

Well Control $115k

Well Delivery Engineer $120k

Build & Release $151k

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Rating 4.1 based on 505 ratings
Length 10 total hours
Starts On Demand (Start anytime)
Cost $12
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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