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Natural Language Processing (NLP): Deep Learning in Python

$39.99 $12.00

4.88 (12 reviews)
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Description

What you’ll 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

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)

Who 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.

Course content

  • Outline, Review, and Logistical Things
  • Beginner’s Corner: Working with Word Vectors
  • Review of Language Modeling and Neural Networks
  • Word Embeddings and Word2Vec
  • Word Embeddings using GloVe
  • Unifying Word2Vec and GloVe
  • Using Neural Networks to Solve NLP Problems
  • Recursive Neural Networks (Tree Neural Networks)
  • Theano and Tensorflow Basics Review
  • Setting Up Your Environment (FAQ by Student Request)

12 reviews for Natural Language Processing (NLP): Deep Learning in Python

  1. Luis Brito

    I can definitely see this course helping breaking through that initial barrier when it comes to learning a programming language. I’m positive I’ll finish it and hopefully master python by then.

  2. Abdallah Galal Abouhussein

    I would like to share my personnel experience, i`m very pleased and happy for taking this impressive course because Dr. Angela always makes it simple and easy to understand and always makes a precaution section before or after any bug that may appear to some students and also write or record the solution for us, and each word she said is very real and easy to understand.
    Thanks Dr. Angela
    Abdallah Galal

  3. Jiaqing Fan

    Very good course. Dr Angela explains things very clearly. The exercises are interactive and helpful.

    Angela’s voice is like ASMR in most lessons. So you won’t lose focus?

  4. Jaime Sánchez Blanco

    Every concept is very well explained so you can assimilate it, and the teacher insists in practicing daily to get the habit of programming. It really motivates to keep studying and practicing every day.

  5. Bradley Carouthers

    3 days in and I’ve already learned more than other training programs where I’ve put months in. Her teaching style is both efficient and effective.

    Edit: Now on day 29 and the course has held up my expectations and hopes. The rise in complexity is not too sharp, and every day feels like an accomplishment. Angela takes her time to explain things without rushing, yet is able to pack tons of information in nuggets of time.

    Still recommend supplemental exposure for a more wholesome grasp. Though, this should be quite enough

  6. Tommy Prévost

    Very good course.

    PROs: lots of coding exercises, clear explanations, great examples, adapted to 2021’s reality.

    CONs: past the first 30 lessons, many similar coding hands-on.

  7. Quang Tu

    I love all the lectures and challenges of this course. I finally find myself in love with coding. This is definitely a fun course. Thank you Angela for this great course.

  8. Fortunate Eze

    This course is nothing short of AMAZING. The level of love and sincere desire to see students succeed present in this course is a rare sight. This course is fun, it’s challenging, it’s suspense-filled, it makes you feel good about yourself solving those challenges. You don’t take this course and memorise codes, you take it and you know what you’re doing because you literally do it yourself.

    It couldn’t possibly be any more obvious that Angela brought out her whole heart, time, energy and resources to give us a course that if priced based on it’s value, many of us certainly can’t afford. I mean, she kept advising and encouraging us to keep going, because she knows the road could get bumpy, she made a few jokes and used funny animations so we smile along the way keeping it fun. Why some people can’t see this and would rather subscribe to trying to bend courses to fit their own personal preferences is beyond me, but I guess people will always be people.

    Dear Angela, I cannot speak for everyone but I certainly can speak for myself and I’m saying thank you. The efforts you put into making this course outstanding didn’t go unnoticed.

    Let as many of us as appreciate your efforts make you happy and energize you to create more life changing courses for us.

    Cheers.

  9. Chendaniel

    Just the course fits me. Learn by doing, it is easy to start and just follow the flow and at day 15 you realized how to program in Python. Then the magic happen. I am on day 27 and I am looking forward to start a new section everyday.

  10. Caspar Cheng

    Angela is my favourite teacher on Udemy, her courses always come with clear and concise demonstration, make it really easy to understand the content. Meanwhile, she also provide a lot of tips to encourage us moving further on the way of coding. I’m definitely a super fan of her. I had finished her web development boot camp and now I’m learning Python 100 days of code. This course is amazing as she provided one particular project for each day’s learning, which is, really useful and handy. Also, those projects are really interesting and won’t let me feel disappointed about myself. Although some of them are a little bit difficult, they’re still resolvable after some struggling and searching online. I’ll recommend this course for those who really want to learn Python well and write real python projects from scratch!

  11. Jonas Weber

    The course was awesome in the first half! Really helped me to get back to programming and learning new techniques. Also the course got way to much focused on web development in the second half, which I’m not really interested in and is redundand to your dedicated web development course. The lack of videos in the last quarter was rather sad. I really would’ve liked some video explanations for pandas and matplotlib. The challenges and capstone projects were great and challenging!
    All in all I can really recommend this course for beginners and those who want to learn python and/or get back to programming!

  12. Rohit Prasad

    Angela is the best teacher I have ever experienced. The way she explains all the topics and makes use of it in the projects is commendable. Thank you so much Angela for this course.

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