Modern Natural Language Processing in Python
Download Modern Natural Language Processing in Python course to Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colaboratory.
What you’ll learn
- Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator)
- Build a CNN specialized in NLP for any classification task (e.g. sentimental analysis)
- Write a custom training process for more advanced training methods in NLP
- Create customs layers and models in TF 2.0 for specific NLP tasks
- Use Google Colab and Tensorflow 2.0 for your AI implementations
- Pick the best model for each NLP task
- Understand how we get computers to give meaning to the human language
- Create datasets for AI from those data
- Clean text data
- Understand why and how each of those models work
- Understand everything about the attention mechanism, lying behind the newest and most powerful NLP algorithms
Check out this Python course for best understanding.
- PC with an Internet connection
- Python Programming Skills
- Recommended: Experience with TF2.0
- Recommended: Google Collab
Description of Modern Natural Language Processing in Python
Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP.
Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs.
Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.
- First, we will dive into CNNs to create a sentimental analysis application.
- Then we will go for Transformers, replacing RNNs, to create a language translation system.
The course is user-friendly and efficient: Modern NL leverages the latest technologies—Tensorflow 2.0 and Google Colab—assuring you that you won’t have any local machine/software version/compatibility issues and that you are using the most up-to-date tools.
Who this course is for:
- AI amateurs that are eager to learn how we process language nowadays
- AI students that need to have a deeper and wider knowledge about NLP
- Business-driven people that are eager to know how NLP can be applied to their field to leverage any text data
- Anyone who wants to start a new career and get a strong background in NLP, adding efficient cases to their portfolio
The explanation of the theories followed by line by line code is done well in this Modern Natural Language Processing in Python course. You will appreciate the course if you know Neural Networks, CNN, RNN as this is not a beginner’s course.
Just an amazing course that explains the technicality as needed and also explains how to convert theory into working code. Could have been more intuitive. I should have stressed a few concepts and explained better, but what was taught in this course is just awesome.
Detailed hands-on exercises at the end of the theory portion were really helpful to understand how to use the application of NLP in solving text-related problems.
The Modern Natural Language Processing in Python course is very informative and has a good amount of hands-on activities. The only thing I would want to be improved is the clarity of speech in a few words
Really good one, I wish we had a bit more explanations on downloading the libraries in pip and use it in python3 which I am trying to do along with this course.
Interesting pretty many things need to be known prior. It’s true since its an advanced course. If the duration of the Modern Natural Language Processing in Python course, it is recommendable for students.
Awesome course. THIS GAVE A DEEP INSIGHT INTO THE CONCEPT OF CNN. DETAILED GOOD PRESENTATION.
Make sure to check out most popular Python Masterclass.
The concept of CNN was briefly explained but nowhere mentioning of Stride and padding was there. From my point of view, that is important because it gives more strength to feature maps to extract more information.
Still, the course is well structured and my motivation to take up this Modern Natural Language Processing in Python course is to build more and more knowledge toward the concepts and apply them on a broader scale.