Natural Language Processing - Deep Learning Models in Python
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Natural Language Processing - Deep Learning Models in Python
This course is part of Modern Natural Language Processing Specialization
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What you'll learn
Implement deep learning models for NLP using Python and TensorFlow.
Understand and apply feedforward, convolutional, and recurrent neural networks for text data.
Build and train models for text classification, NER, and POS tagging.
Learn advanced techniques such as CBOW and LSTM for improving NLP tasks.
Skills you'll gain
Tools you'll learn
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There are 6 modules in this course
Updated in May 2025.
This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will learn how to apply deep learning models to Natural Language Processing (NLP) tasks using Python. By the end of the course, you will be able to understand and implement cutting-edge deep learning models, including Feedforward Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks, tailored for NLP applications. You will also get hands-on experience with text classification, embeddings, and advanced models such as CBOW, GRU, and LSTM in TensorFlow. The course begins by providing a strong foundation, where you will understand the basic concepts of neural networks and their role in NLP. You will then move on to implement text classification using TensorFlow, exploring both the mathematical foundations of neurons and the practical implementation aspects. As the course progresses, you will dive deeper into more advanced models such as convolutional and recurrent neural networks. You will explore the theoretical background and code implementations for each of these models, ensuring that you gain both knowledge and practical skills. The second half of the course focuses on advanced topics like embeddings, CBOW, and recurrent neural networks (RNNs). You will explore how RNNs are used for sequential data processing, implementing tasks such as Named Entity Recognition (NER) and Parts-of-Speech (POS) tagging. Additionally, you'll tackle practical exercises that challenge you to apply your knowledge of convolutional and recurrent neural networks to real-world NLP tasks, further enhancing your skill set. This course is designed for individuals looking to deepen their understanding of NLP using deep learning models. It is suitable for anyone interested in the intersection of Python programming, deep learning, and natural language processing. While a basic understanding of Python is recommended, no prior experience in deep learning is required. The course will progress at a steady pace, offering both theoretical insights and hands-on coding practice.
In this module, we will introduce you to the course and give a detailed outline of the journey ahead. We will also walk through the special offer exclusive to participants, ensuring you are set up for success in the course.
What's included
2 videos2 readings
2 videos•Total 8 minutes
- Introduction and Outline•7 minutes
- Special Offer•1 minute
2 readings•Total 20 minutes
- Introduction to the Course 'Natural Language Processing - Deep Learning Models in Python'•10 minutes
- Full Course Resources•10 minutes
In this module, we will show you how to find and download the necessary resources to get started. We'll also share useful tips to help you navigate through the course with confidence and make the most of your learning experience.
What's included
2 videos1 assignment
2 videos•Total 6 minutes
- Where To Get the Code•3 minutes
- How To Succeed in This Course•3 minutes
1 assignment•Total 15 minutes
- Getting Set Up - Assessment•15 minutes
In this module, we will explore the fundamentals of the neuron, focusing on its mathematical foundations and role in deep learning. Key topics include text classification, fitting lines to data, and understanding how models learn during training.
What's included
7 videos1 assignment
7 videos•Total 59 minutes
- The Neuron - Section Introduction•2 minutes
- Fitting a Line•14 minutes
- Classification Code Preparation•7 minutes
- Text Classification in Tensorflow•12 minutes
- The Neuron•10 minutes
- How does a model learn?•11 minutes
- The Neuron - Section Summary•2 minutes
1 assignment•Total 15 minutes
- The Neuron - Assessment•15 minutes
In this module, we will dive into feedforward artificial neural networks, focusing on their architecture, mechanisms like forward propagation, and the crucial role of activation functions. We will also demonstrate how to apply these concepts to text classification tasks.
What's included
15 videos1 assignment
15 videos•Total 122 minutes
- ANN - Section Introduction•7 minutes
- Forward Propagation•10 minutes
- The Geometrical Picture•10 minutes
- Activation Functions•17 minutes
- Multiclass Classification•9 minutes
- ANN Code Preparation•5 minutes
- Text Classification ANN in Tensorflow•6 minutes
- Text Preprocessing Code Preparation•12 minutes
- Text Preprocessing in Tensorflow•6 minutes
- Embeddings•10 minutes
- CBOW (Advanced)•4 minutes
- CBOW Exercise Prompt•1 minute
- CBOW in Tensorflow (Advanced)•19 minutes
- ANN - Section Summary•2 minutes
- Aside: How to Choose Hyperparameters (Optional)•6 minutes
1 assignment•Total 15 minutes
- Feedforward Artificial Neural Networks - Assessment•15 minutes
In this module, we will cover the theory and practical applications of convolutional neural networks, emphasizing their use in NLP. From understanding convolution to implementing CNNs for text processing in TensorFlow, this module prepares you for more advanced tasks.
What's included
9 videos1 assignment
9 videos•Total 86 minutes
- CNN - Section Introduction•5 minutes
- What is Convolution?•17 minutes
- What is Convolution? (Pattern Matching)•6 minutes
- What is Convolution? (Weight Sharing)•7 minutes
- Convolution on Color Images•16 minutes
- CNN Architecture•21 minutes
- CNNs for Text•8 minutes
- Convolutional Neural Network for NLP in Tensorflow•6 minutes
- CNN - Section Summary•1 minute
1 assignment•Total 15 minutes
- Convolutional Neural Networks - Assessment 4•15 minutes
In this module, we will dive into recurrent neural networks (RNNs), exploring how they process sequential data and their application in NLP tasks. We will also introduce advanced models like GRU and LSTM, guiding you through real-world implementations in TensorFlow.
What's included
12 videos1 reading3 assignments
12 videos•Total 107 minutes
- RNN - Section Introduction•5 minutes
- Simple RNN / Elman Unit (pt 1)•9 minutes
- Simple RNN / Elman Unit (pt 2)•10 minutes
- RNN Code Preparation•10 minutes
- RNNs: Paying Attention to Shapes•8 minutes
- GRU and LSTM (pt 1)•18 minutes
- GRU and LSTM (pt 2)•12 minutes
- RNN for Text Classification in Tensorflow•6 minutes
- Parts-of-Speech (POS) Tagging in Tensorflow•20 minutes
- Named Entity Recognition (NER) in Tensorflow•5 minutes
- Exercise: Return to CNNs (Advanced)•3 minutes
- RNN - Section Summary•2 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Natural Language Processing - Deep Learning Models in Python'•10 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Recurrent Neural Networks - Assessment•15 minutes
- Full Course Assessment•60 minutes
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Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
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