Practical NLP & DL: From Text to Neural Networks (12+ Hours)

This course is designed for anyone eager to dive into the exciting world of Natural Language Processing (NLP) and Deep Learning, two of the most rapidly growing and in-demand domains in the artificial intelligence industry. Whether you're a student, a working professional looking to upskill, or an aspiring data scientist, this course equips you with the essential tools and knowledge to understand how machines read, interpret, and learn from human language. We begin with the foundations of NLP, starting from scratch with text preprocessing techniques such as tokenization, stemming, lemmatization, stopword removal, POS tagging, and named entity recognition.

These techniques are critical for preparing unstructured text data and are used in real-world AI applications like chatbots, translators, and recommendation engines. Next, you will learn how to represent text in numerical form using Bag of Words, TF-IDF, One-Hot Encoding, N-Grams, and Word Embeddings like Word2Vec. These representations are a bridge between raw text and machine learning models.

As the course progresses, you will gain hands-on experience with Neural Networks, understanding concepts such as perceptrons, activation functions, backpropagation, and multilayer networks. We'll also explore CNNs (Convolutional Neural Networks) for spatial data and RNNs (Recurrent Neural Networks) for sequential data like text. The course uses Python as the primary programming language and is beginner-friendly, with no prior experience in NLP or deep learning required.

By the end, you'll have practical experience building end-to-end models and the confidence to apply your skills in real-world AI projects or pursue careers in machine learning, data science, AI engineering, and more.