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Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets
4.6
4.6/5
(6,525 reviews)
40,012 students
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9.3

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.3

Content

Platform: Udemy
Video: 11h 59m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 5/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.2 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.3 / 10
Video Score: 9.4 / 10
The course includes 11h 59m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 7 hours 24 minutes of 1,300 Python courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
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0 test.

Table of contents

Description

In this course we are going to look at NLP (natural language processing) with deep learning.
Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.
These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.
In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.
First up is word2vec.
In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.
Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:
•king – man = queen – woman
•France – Paris = England – London
•December – Novemeber = July – June
For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.
Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.
We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.
Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.
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.

See you in class!

“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…

Suggested Prerequisites:
•calculus (taking derivatives)
•matrix addition, multiplication
•probability (conditional and joint distributions)
•Python coding: if/else, loops, lists, dicts, sets
•Numpy coding: matrix and vector operations, loading a CSV file
•neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own
•Can write a feedforward neural network in Theano or TensorFlow
•Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function
•Helpful to have experience with tree algorithms

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
•Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

You will learn

✓ Understand and implement word2vec
✓ Understand the CBOW method in word2vec
✓ Understand the skip-gram method in word2vec
✓ Understand the negative sampling optimization in word2vec
✓ Understand and implement GloVe using gradient descent and alternating least squares
✓ Use recurrent neural networks for parts-of-speech tagging
✓ Use recurrent neural networks for named entity recognition
✓ Understand and implement recursive neural networks for sentiment analysis
✓ Understand and implement recursive neural tensor networks for sentiment analysis
✓ Use Gensim to obtain pretrained word vectors and compute similarities and analogies

Requirements

• Install Numpy, Matplotlib, Sci-Kit Learn, and Theano or Tensor
• Flow (should be extremely easy by now)
• Understand backpropagation and gradient descent, be able to derive and code the equations on your own
• Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
• Code a feedforward neural network in Theano (or Tensorflow)
• Helpful to have experience with tree algorithms

This course is for

• Students and professionals who want to create word vector representations for various NLP tasks
• Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
• SHOULD NOT: Anyone who is not comfortable with the prerequisites.

How much does the Natural Language Processing with Deep Learning in Python course cost? Is it worth it?

The course costs $39.99.
The average price is $19.0 of 1300 Python courses on Udemy.

Does the Natural Language Processing with Deep Learning in Python course have a money back guarantee or refund policy?

YES, Natural Language Processing with Deep Learning in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

At the moment we could not find an available scholarship for Natural Language Processing with Deep Learning in Python .

Who is the instructor? Is Lazy Programmer Team a SCAM or a TRUSTED instructor?

Lazy Programmer Team has created 15 courses that got 44,096 reviews which are generally positive. Lazy Programmer Team has taught 173,868 students and received a 4.6 average review out of 44,096 reviews. Depending on the information available, Lazy Programmer Team is a TRUSTED instructor.
Artificial Intelligence and Machine Learning Engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.

9.3

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.3

Content

Platform: Udemy
Video: 11h 59m
Language: English
Next start: On Demand

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