Certified Specialist Programme in Machine Learning for Sentiment Analysis
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- Introduction to Sentiment Analysis and its Applications
- Text Preprocessing for Sentiment Analysis: Tokenization, Stemming, Lemmatization
- Feature Engineering for Sentiment Analysis: Bag-of-Words, TF-IDF, Word Embeddings
- Machine Learning Algorithms for Sentiment Analysis: Naive Bayes, Logistic Regression, Support Vector Machines
- Deep Learning Methods for Sentiment Analysis: Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Transformers
- Sentiment Analysis Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
- Handling Noisy Data and Ambiguity in Sentiment Analysis
- Advanced Topics in Sentiment Analysis: Aspect-Based Sentiment Analysis, Cross-Lingual Sentiment Analysis
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Career Roles in Machine Learning for Sentiment Analysis (UK) Description Machine Learning Engineer (Sentiment Analysis) Develops and implements machine learning models for sentiment analysis, focusing on natural language processing (NLP) and deep learning techniques.
High industry demand.
Data Scientist (Sentiment Analysis Focus) Analyzes large datasets to extract insights related to sentiment, using advanced statistical methods and machine learning algorithms.
Strong analytical skills required.
NLP Specialist (Sentiment Analysis) Specializes in natural language processing, particularly in building and improving sentiment analysis systems.
Expertise in NLP libraries and techniques essential.
AI Consultant (Sentiment Analysis) Advises businesses on implementing sentiment analysis solutions to improve customer understanding and business strategies.
Requires strong communication and business acumen.
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- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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