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.
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
Who is it for?
- Analytics professionals, modellers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand or learn machine learning and apply it to problems they are solving
- Tech executives and investors who are interested in big data, machine learning or natural language processing
- Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
This course consists of the following modules:
- Module 1: What are You Feeling Like?
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.
Successful candidates will be awarded a certificate for Machine Learning – Twitter Sentiment Analysis in Python.
- Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
- Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code.
This course will provide you with the knowledge and skills to gain high level job roles in the following industries:
- Machine learning
- Quantitative research
- Risk analysis
- Quantitative trading
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
PLEASE NOTE: We do not provide any software with this course.
|Machine Learning – Twitter Sentiment Analysis in Python||FREE||00:00:00|
|1: What are You Feeling Like?|
|1. Introduction: You, This Course &Us!||00:00:00|
|2. Sentiment Analysis: What’s all the fuss about?||00:00:00|
|3. Machine Learning Solutions for Sentiment Analysis: the devil is in the details||00:00:00|
|4. Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)||00:00:00|
|5. Installing Python – Anaconda and Pip||00:00:00|
|6. Back to Basics: Numpy in Python||00:00:00|
|7. Back to Basics: Numpy & Scipy in Python||00:00:00|
|8. Regular Expressions||00:00:00|
|9. Regular Expressions in Python||00:00:00|
|10. Put it to work: Twitter Sentiment Analysis||00:00:00|
|11. Twitter Sentiment Analysis: Work the API||00:00:00|
|12. Twitter Sentiment Analysis: Regular Expressions for Preprocessing||00:00:00|
|13. Twitter Sentiment Analysis: Naive Bayes, SVM & SentiWordNet||00:00:00|
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