Natural Language Processing (NLP) is a hot topic into Machine Learning field.

This course is an advanced course of NLP using Deep Learning approach.

Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.

This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow 1.X CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate fastly the training processes of your models. However if you dont have a GPU card you can follow the instructions using Google Colab.

After that we are going to review the main concepts of Deep Learning in the Chapter 2 for applying them into the Natural Language Processing field offering you a solid background for the main chapter.

In the main Chapter 3 we are going to study the main Deep Learning libraries and models for NLP such as:

- Word Embeddings,

- Word2Vec,

- Glove,

- FastText,

- Universal Sentence Encoder,

- RNN,

- GRU,

- LSTM,

- Convolutions in 1D,

- Seq2Seq,

- Memory Networks,

- and the Attention mechanism.

This course offers you many examples, with different datasets suchs as:

- Google News,

- Yelp comments,

- Amazon reviews,

- IMDB reviews,

- the Bible corpus, etc and different text corpus.

At the final in Chapter 4 you will put in practice your knowledge with practical applications such as:

- Multiclass Sentiment Analysis,

- Text Generation,

- Machine Translation,

- Developing a ChatBot and more.

For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.

If you need a previous background in Natural Language Processing or in Machine Learning I recommend you my courses:

• Python for Machine Learning and Data Mining or

• Natural Language Processing with Python and NLTK

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: [email protected] or by Twitter: @AILearningCQ