This Natural Language Processing (NLP) live training series by Krish Naik provides a complete practical journey into modern NLP and deep learning techniques. The course is structured as a step-by-step learning path that starts from basic concepts and moves toward advanced neural network architectures.
It begins with an introduction to NLP and a roadmap for understanding how language data is processed in machine learning systems. Learners are introduced to classical text representation methods such as Bag of Words and TF-IDF, which are essential for converting text into numerical features.
The series then progresses into word embeddings and Word2Vec models, including CBOW and Skip-gram architectures. These techniques help machines understand semantic relationships between words in a more meaningful way.
Advanced sections cover training Word2Vec from scratch and building intuition around distributed word representations. The course then transitions into deep learning models, starting with Recurrent Neural Networks (RNNs), backpropagation through time, and their applications in NLP.
Further topics include Long Short-Term Memory (LSTM) networks, word embedding layers, and practical implementations of LSTM-based NLP models. Each session combines theory with hands-on examples to strengthen understanding.
By the end of this series, learners gain a strong foundation in both traditional NLP techniques and modern deep learning approaches used in real-world AI systems.
