Natural language processing (NLP) is the field of computer science that focuses on enabling machines to understand and use human language. It is a subset of artificial intelligence (AI) that has grown significantly in recent years with the help of deep learning algorithms. Today, many NLP problems, which were once challenging, can be easily solved by using machine learning techniques.
Course Overview
The Natural Language Processing with Deep Learning in Python course is a complete guide for deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets. It is designed for students who have a basic understanding of machine learning and data science, and who want to build their knowledge of NLP with deep learning.
The course has gained an aggregate rating of 4.61361 from 7655 students, indicating that it is a widely appreciated course with high-quality content.
What You Can Learn
The course is a comprehensive guide with four major areas of focus. These are:
1. Word2Vec
The course starts by demonstrating how Word2Vec works. Word2Vec is a popular algorithm that maps words to a vector space where you can find analogies. The course covers the theory, implementation, and application of Word2Vec. It also shows a demo of classifying news articles based on their titles and content.
Moreover, for the beginners who are finding algorithms tough, the course also demonstrates the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies.
2. GloVe Method
The GloVe method is also introduced in the course, which is used to find word vectors but uses a technique called matrix factorization. It is also a popular algorithm for recommender systems. Interestingly, the word vectors produced by GloVe are just as good as the ones produced by Word2Vec and are easier to train.
3. Recurrent Neural Networks (RNN)
The course then moves on to RNNs, which are used in solving traditional NLP problems like parts-of-speech tagging and named entity recognition. It also covers how RNNs can be utilized to solve other similar problems. By the end of this section, the students should be able to construct a network that can classify movie reviews based on their sentiment.
4. Recursive Neural Networks (RecNN)
The last part of the course covers RecNN, which enables the machines to solve the problem of negation in sentiment analysis. Recursive neural networks assume that sentences have a tree structure, thus allowing us to get away from naively using bag-of-words.
Prerequisites To Take Course
The course expects students to have a prior understanding of:
- 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, the ability to derive and code gradient descent algorithms to solve optimization problems.
- The ability to write a feedforward neural network in Theano or TensorFlow
- The ability to write a recurrent neural network/LSTM/GRU in Theano or TensorFlow from basic primitives, especially the scan function
- Experience with tree algorithms is helpful
Unique Features
- Every line of code is explained in detail, so students can learn at their pace.
- The course does not waste time "typing" on the keyboard like other courses; instead, it emphasizes learning how to implement these algorithms from scratch.
- The course is not afraid of university-level math. It provides important details about algorithms that other courses tend to skip.
The Instructor
The instructor behind this course is Dr. LazyProgrammer, who has over 20 years of experience as a course review content writer for top-ranking course review sites. His courses are the only ones that teach students how to implement machine learning algorithms from scratch. In his courses, students can learn how to visualize what is happening in the model internally instead of merely remembering facts. His teaching style is unique, engaging, and is designed to help students learn more than just the basics of NLP with deep learning.
Concluding Thoughts
The Natural Language Processing with Deep Learning in Python course is a complete guide that covers an array of quintessential concepts in NLP with Deep Learning. The course's ratings and the number of reviews showcase that it is appreciated by the students and that it provides value. The course instructor's unique teaching philosophy adds another layer of depth to the course. Enrolling in this course would be a wise investment for students interested in NLP with deep learning, looking to build their knowledge from scratch.
