Natural Language Processing with Deep Learning
( 30 Modules )

Module #1
Introduction to NLP
Overview of NLP, its applications, and importance

Module #2
Mathematical Foundations
Review of Linear Algebra, Calculus, and Probability Theory

Module #3
Deep Learning Fundamentals
Introduction to Deep Learning, neural networks, and PyTorch/TensorFlow

Module #4
Text Preprocessing
Tokenization, token normalization, stop words, and stemming/lemmatization

Module #5
Word Embeddings
Word2Vec, GloVe, and FastText for vector representations

Module #6
Recurrent Neural Networks (RNNs)
Introduction to RNNs, Simple RNN, LSTM, and GRU

Module #7
Long Short-Term Memory (LSTM) Networks
In-depth exploration of LSTM architecture and applications

Module #8
Gated Recurrent Units (GRU)
GRU architecture and comparisons with LSTM

Module #9
Sequence-to-Sequence Models
Introduction to seq2seq models, encoder-decoder architecture

Module #10
Attention Mechanisms
Introduction to attention, self-attention, and multi-head attention

Module #11
Convolutional Neural Networks (CNNs) for NLP
Applying CNNs to NLP tasks, text classification, and sentiment analysis

Module #12
Text Classification
Binary and multi-class text classification using DL models

Module #13
Sentiment Analysis
Binary and multi-class sentiment analysis using DL models

Module #14
Named Entity Recognition (NER)
Introduction to NER, entity recognition using DL models

Module #15
Language Modeling
Introduction to language modeling, n-gram models, and DL-based models

Module #16
Machine Translation
Introduction to machine translation, sequence-to-sequence models, and attention

Module #17
Question Answering and Dialogue Systems
Introduction to QA, dialogue systems, and chatbots

Module #18
Advanced Topics in NLP
Transformer architecture, BERT, and other state-of-the-art models

Module #19
Explainability and Interpretability in NLP
Introduction to model interpretability and explainability techniques

Module #20
NLP for Specialized Domains
Applying NLP to healthcare, finance, and other specialized domains

Module #21
NLP for Low-Resource Languages
Challenges and opportunities in NLP for low-resource languages

Module #22
Ethical Considerations in NLP
Ethical implications of NLP, bias, and fairness

Module #23
NLP Project Development
Guided project development using NLP and DL techniques

Module #24
NLP in Industry and Research
Applications of NLP in industry and research, including case studies

Module #25
Advancements in NLP
Recent advancements and future directions in NLP

Module #26
NLP for Multimodal Data
Applying NLP to multimodal data, including text, images, and videos

Module #27
NLP for Multilingual Data
Applying NLP to multilingual data, including machine translation and language identification

Module #28
NLP for Noisy or Unstructured Data
Applying NLP to noisy or unstructured data, including handling out-of-vocabulary words

Module #29
NLP for Real-World Applications
Case studies of NLP applications in real-world scenarios

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Natural Language Processing with Deep Learning career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Natural Language Processing with Deep Learning ( 30 Modules )

Natural Language Processing with Deep Learning
( 30 Modules )