Sentiment Analysis for Machine Learning in AI Content
-- viewing nowSentiment Analysis is a crucial technique in Natural Language Processing (NLP) and Machine Learning (ML). It automatically identifies and extracts subjective information from text, such as opinions, emotions, and attitudes.
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Course details
• Part-of-Speech Tagging
• Named Entity Recognition
• Lexicon-based Sentiment Scoring
• Machine Learning Models (e.g., SVM, Naive Bayes, LSTM)
• Feature Engineering (e.g., n-grams, word embeddings)
• Sentiment Intensifiers and Negation Handling
• Contextual Understanding and Disambiguation
• Performance Evaluation Metrics (e.g., Accuracy, Precision, Recall, F1-score)
• Data Preprocessing (e.g., cleaning, stemming, lemmatization)
Career path
Sentiment Analysis: UK Machine Learning Job Market
Role | Description | Avg. Salary (£) |
---|---|---|
Machine Learning Engineer | Develops and implements ML algorithms; strong programming & data science skills. | 60000 |
Data Scientist (Machine Learning Focus) | Analyzes large datasets, builds predictive models using ML techniques. | 55000 |
AI/ML Consultant | Provides expert advice on AI/ML strategies, implementation, and optimization. | 75000 |
NLP Engineer (Sentiment Analysis) | Specializes in natural language processing, focusing on sentiment analysis. | 65000 |
Research Scientist (Machine Learning) | Conducts research and develops novel ML algorithms and techniques. | 70000 |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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