Machine Learning – Twitter Sentiment Analysis in Python
- Ratings
- 4.0
- Last updated 3 years ago
- ENGLISH
AED 145
AED 745
Learn why Sentiment Analysis is useful and how to approach the problem using both Rule-Based and Machine Learning-Based approaches. The details are really important – training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter Sentiment Analysis. We’ll spend some time on Regular Expressions which are pretty handy to know as we’ll see in our code-along.
Course Duration:
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
Method of Assessment:
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.
Certification:
Successful candidates will be awarded a certificate for Machine Learning – Twitter Sentiment Analysis in Python.
Benefits you will gain:
By enrolling in this course, you’ll get:
✓ High-quality e-learning study materials and mock exams.
✓ Tutorials/materials from the industry leading experts.
✓ Includes step-by-step tutorial videos and an effective, professional support service.
✓ 24/7 Access to the Learning Portal.
✓ The benefit of applying for NUS extra Discount Card.
✓ Recognised Accredited Qualification.
✓ Access Course Content on Mobile, Tablet or Desktop.
✓ A study in a user-friendly, advanced online learning platform.
✓ Excellent customer service and administrative support.
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