❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 31st, 2023]

Skills and Knowledge:
By the end of this course, you will have acquired the following skills and knowledge:
- Understanding of word embeddings and how to apply them for text classification
- Knowledge of 1D convolutions and how to use them as feature extractors in NLP
- Ability to perform binary text classification using deep learning
- Proficiency in using Tensorflow or Keras to build deep learning models
- Experience in working with the Toxic Comment Classification Challenge on Kaggle

Professional Growth:
This course contributes to professional growth by providing learners with the following skills and knowledge:
1. Text Classification: The course teaches learners how to classify text using 1D convolutions with Keras. This skill is highly valuable in various industries, such as marketing, customer service, and sentiment analysis, where understanding and categorizing text data is crucial.
2. Word Embeddings: Learners will learn how to apply word embeddings for text classification. Word embeddings are a powerful technique in natural language processing (NLP) that represent words as dense vectors, capturing semantic relationships between words. Understanding and applying word embeddings is essential for working with text data in NLP tasks.
3. Feature Extraction: The course teaches learners how to use 1D convolutions as feature extractors in NLP. Convolutional neural networks (CNNs) are commonly used in computer vision tasks, but they can also be applied to NLP tasks. Understanding how to use convolutions for feature extraction in NLP expands learners' toolkit for solving text classification problems.
4. Deep Learning: The course focuses on performing binary text classification using deep learning techniques. Deep learning has revolutionized many fields, including NLP, and being proficient in deep learning is highly sought after in the job market. By completing this course, learners will gain hands-on experience in building deep learning models for text classification.
5. Real-world Case Study: The course uses a real-world case study of classifying toxic comments on Wikipedia. This case study allows learners to apply the skills and techniques learned in a practical context. It also exposes learners to the challenges and considerations involved in content moderation, online harassment, and inclusivity, which are important topics in today's digital landscape.
Overall, this course equips learners with practical skills in text classification, NLP, deep learning, and provides them with a real-world case study to apply their knowledge. These skills and experiences contribute to their professional growth and make them more competitive in the job market, particularly in roles that involve working with text data and NLP.

Further Education:
This course is suitable for preparing for further education. It provides a guided introduction to text classification using 1D convolutions with Keras, which is a popular deep learning framework. By completing this project, you will gain knowledge and skills in applying word embeddings for text classification, using 1D convolutions as feature extractors in natural language processing (NLP), and performing binary text classification using deep learning. These are valuable skills for further education in the field of NLP and deep learning. However, it is recommended that you have prior experience in Python programming, deep learning theory, and have used either Tensorflow or Keras to build deep learning models before taking this course.