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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.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • 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?

  • 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...

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Rating 4.1 based on 594 ratings
Length 12 total hours
Starts On Demand (Start anytime)
Cost $16
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

Pacing is perfect most especially for viewers with prior knowledge in Machine Learning and user interface of sklearn.

Great for beginners to know about nlp with machine learning and regular expression.

It's good for learning how to process text data and apply them to machine learning.

I like the real world workflow of getting and processing data before using machine learning.

Appreciated the machine learning review section too.

I am from a Linguistics background yet I understand everything explained Its Good This was not a tutorial just a rush follow along with video Explanation is very good and the design / order of content is excellent It was a great experiencing having to dive deeper into the Natural Language Preprocessing in Machine Learning.

Ideally, it could have more real word applications and some practical advices and details about when and how to implement machine learning algorithms.

May be more interesting by introducing 1 or 2 more challenging NLP problems or topics (NLU, NLG, etc...) If I have a little experience of machine learning, it's more through its application to computer vision.

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

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

The machine learning review was so useful.

The course is a good introduction, with a basic Machine Learning knowledge it was quite simple.

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

Simple application of machine learning including classification, eigendecomposition and markov modeling.

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lazy programmer

Looking forward to lazy programmer's nlp course with deep learning!

Lazy programmer is a great teacher with a knack for explaining topics in a simple way and getting you outside your comfort zone.

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

Thanks to the lazy programmer for putting the efforts in to make this course.

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.

I've taken a number of the Lazy Programmer's courses and enjoy his laid back style and presentation.

Looking forward to take other courses on deep learning so that i complete all prerequisite before taking Advance NLP by Lazy Programmer.

Lazy programmer is a great instructor and is able to convey the concepts in an intuitive way.

Lazy Programmer is a great instructor with great experience in the best way to learn the subject.

As always, the Lazy Programmer does not disappoint.

Thanks to lazy programmer for showing us simple applications of NLP.

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easy to follow

Not easy to follow instructor, rather monotonous.

good easy to follow and not to fall sleep so far slides are not so great and the content is average Pros: The level of material is as advertised.

Codes are short 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.

Yes it seems good for beginning concise and well documented This course gives a pretty good overview of NLP Good start for newbies Easy to follow and merges and advanced concepts Useful course projects with hands on experience The course explains why and how clearly Excellent for base concepts in NLP in python.

liked what i have seen so far Practical examples that are easy to follow Not much theory, not much content or coding examples being considered More explanations needed and less watching code being typed.

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natural language processing

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

I got introduced to natural language processing in a speedy and concise way.

This was a good introduction 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.

Course was a good overview of natural language processing.

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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.

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.

In sentiment analysis he can include unsupervised learning like obtain negative, positive percent as well rather than just considering as text classification problem.

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spam detection

It had encryption breaking, spam detection, article spinning, and other practical applications.

Cuts to the chase quickly with spam detection example.

Very useful and true to the curriculum it teaches some NLP applications - spam detection, sentiment analysis, article spinning, ... After intense learning and excellent instruction, I get a little sense of machine learning now.

The spam detection exercise shows it is easy to do nlp in a plug and play manner.

The first example on spam detection was very simply plugging a dataset into scitkits.

You get a lot of practical exercises like spam detection article spinning.

For example the coverage of spam detection consisted only of training an existing sample model, with no discussion of either how to develop the model or how to use it in practice to actually do spam detection.

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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 Fantastic course!

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recommend this course

I would recommend this course.

I would recommend this course if you want to learn about existing NLP applications, but not if you want to learn how to apply NLP yourself.

I would highly recommend this course.

I would totally recommend this course.

Would definiately recommend this course to professionals in the data science space The course was well organized and the instructor was very well prepared.

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good so far

Amazing it is good so far ... so far so good Very informative The course gave a complete and useful introduction to NLP.

Content is good so far, but the monotonous flat tone of the speaker is making it difficult to concentrate a little The course is almost fine.

Less knowledge sharing I like the gradual intro good so far For the moment, not that interesting a good introduction to NLP Great course, thank You!

till now it is good so far so good good so far Looks very interesting!

Followable Very Good good so far Great introduction it is good gpood excellent very slow ok best 良い .

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latent semantic

unsupervised learning for NLP) like: - Probabilistic Latent Semantic Analysis - Latent Dirichlet Allocation (generative model) Very practical approach of the subject.

It covers Markov Models, using NLTK, using ML to classify /regress based on text (spam detection / sentiment analysis) and Latent Semantic Analysis which is just Principal Components Analysis on text.

Course felt shallow, only few coding examples / applications (3, not counting latent semantic analysis) were discussed, in total includes only 3 hours of lectures.

The latent semantic exercise had interesting results too, when you could see the science and arts split on different areas of the chart.

The latent semantic analysis section shows how to use exploratory data analysis with nlp.

the sentiment analysis example was extremely helpful as was the the latent semantic analysis example.

You will have the opportunity to do spam detection, latent semantic analysis, NLTK, and article spinning.

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real life

I would also suggest the trainer should come up few sessions on Kaggle NLP competitions and at least one full flagged real life NLP case studies till production deployment.

I was looking for a precise and good course over NLP and luckily this course gave me enough confidence to solve real life problems with NLP It was basic but understandable given it was created as a starting point.

It's great to have many practice attempts with real life data.

It helps a lot to do exercises which are connected to real life activities.

Well taught and applicable to real life.

simple explanations with real life tips and helpful exercises Description overstates what the course covers.

Encouraging to learn NLP rather than scaring people away with math excellent teaching by giving real life examples Nice course I like it!

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article spinner

- It's ok that not math in the focus, but if you using equations, it would be useful to recommend books/course helps to understand to linear analitic I LEARNED NOTHING FROM THIS COUSE.. HALF OF TIME INSTRUCTOR IS MARKETING HIS OTHER COURSE.. AND ARTICLE SPINNER MADE BY HIM IS NOT WORKING For beginners I would recommend to start with lecture 11 and then 10.The tutor has put sincere effort to ensure all the participants are benefited from the course.

It kept jumping between NLP applications (article spinner), unexplained statistical theory (Markov models), poorly explained NLP theory concepts (n-gram models), and useless Python code snippets.

The one about the article spinner was particularly fun and interesting Slow to begin/build up, a bit general but to be expected for an intro course.

The article spinner results are pretty funny, but it's understandable since this course isn't on deep learning, which might be required to get an improvement.

The chapter on article spinner need more explanation Fun course for someone who is new to NLP and is familiar with some basic machine learning and coding.

:) Lazy programmer does a fantastic job of explaining complex concepts in simple terms Not very detailed neither in the code part or the theory Every section teaches you tons of new coding techniques, I am excited now to build my own article spinner!

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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 594 ratings
Length 12 total hours
Starts On Demand (Start anytime)
Cost $16
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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