NLP with Deep Learning

Word embeddings (Word2Vec, GloVe), semantic analysis, and building sentiment analysis models.

1. 1Semantics and Sentiment Analysis Part 1Free
2. 2Semantics and Sentiment Analysis Part 2
3. 3Semantics and Sentiment Analysis Part 3
4. 4Semantics and Sentiment Analysis Part 4
5. 5Semantics and Sentiment Analysis Part 5
6. 6Semantics and Sentiment Analysis Part 6

Topic modelling techniques: Latent Dirichlet Allocation (LDA), topic visualization, and interpreting topic model results.

1. 1Topic Modelling Part 1Free
2. 2Topic Modelling Part 2
3. 3Topic Modelling Part 3
4. 4Topic Modelling Part 4
5. 5Topic Modelling Part 5
6. 6Topic Modelling Part 6

Introduction to deep learning for NLP: word embedding layers, RNN architecture, and sequence-to-sequence models.

1. 1Intro to DL for NLP Part 1Free
2. 2Intro to DL for NLP Part 2
3. 3Intro to DL for NLP Part 3
4. 4Intro to DL for NLP Part 4

Advanced architectures: LSTM and GRU for handling long-term dependencies, and building text generation models.

1. 1LSTMs, GRU, Text Generation Part 1Free
2. 2LSTMs, GRU, Text Generation Part 2
3. 3LSTMs, GRU, Text Generation Part 3
4. 4LSTMs, GRU, Text Generation Part 4

Chatbot portfolio project: build a chatbot using deep learning from architecture design to implementation.

1. 1Project Portfolio 12 – Chatbots