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Certificate Programme in Deep Learning for Text Classification
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CourseDetails
- Introduction to Deep Learning for Text Classification
- Natural Language Processing (NLP) Fundamentals for Deep Learning
- Word Embeddings and Vector Representations (Word2Vec, GloVe, FastText)
- Recurrent Neural Networks (RNNs) for Text Classification
- Convolutional Neural Networks (CNNs) for Text Classification
- Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Units (GRUs)
- Attention Mechanisms in Deep Learning for Text
- Deep Learning Model Evaluation Metrics and Optimization
- Transfer Learning and Pre-trained Models for Text Classification
CareerPath
Career Role Description Deep Learning Engineer (Text Classification) Develop and implement advanced text classification models using deep learning techniques.
High industry demand for expertise in NLP and TensorFlow/PyTorch.
NLP Scientist (Text Analytics) Research and develop novel approaches to text analysis and classification; build and deploy cutting-edge solutions for text-based data processing.
Strong analytical and problem-solving skills required.
Machine Learning Engineer (Text Processing) Design and implement efficient and scalable machine learning pipelines for various text-related tasks, including classification, summarization, and generation.
Familiarity with cloud platforms (AWS, GCP, Azure) beneficial.
Data Scientist (Deep Learning and NLP) Extract insights from textual data using deep learning and NLP techniques; build predictive models to solve business problems.
Requires strong statistical background and data visualization skills.
EntryRequirements
- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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