This beginner-friendly Natural Language Processing (NLP) course focuses on building real-world NLP applications using TensorFlow 2, one of the most powerful deep learning frameworks. The course is designed for learners who want to understand how neural networks can be applied to text data and language-based problems.

It begins with an introduction to NLP fundamentals and explains how text is processed and converted into numerical formats that machine learning models can understand. You will learn essential preprocessing steps such as tokenization, padding, and text vectorization, which are crucial for preparing data for deep learning models.

The course then moves into building neural network-based NLP models using TensorFlow 2 and Keras. You will explore how to design and train models for tasks like text classification and sentiment analysis. It also explains how embeddings are used to represent words in a meaningful vector space, improving model performance.

By working through practical examples, you will gain hands-on experience in implementing NLP pipelines from scratch. The course emphasizes simplicity and clarity, making it suitable for beginners in machine learning and deep learning.

By the end, you will be able to build and train basic NLP models using TensorFlow 2 and understand how deep learning is applied to natural language tas