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).
Why you should consider learning at Study 365?
With so many commitments in our lives, we may not have the time to learn new skills. The beauty of studying online is that you get to find this balance between your education and your daily commitments
Study 365 offers hundreds of online courses to students across the world. Gaining new skills has never been this easy as many of our courses are open to students with little or no qualifications or previous experience. All the courses are up-to-date, relevant, affordable, and will build on existing expertise or give you a jump-start to a new career. The courses are meticulously designed and equals what is offered in a classroom setting. With a solid reputation that has established and made us made us one of the most trusted and reliable online course providers we offer the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in your chosen career. You can learn at your own pace at Study 365 and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules, interactive quizzes and free resources which include Free CV writing pack, free career support and course demo to make your learning experience enriching and more rewarding.
- Have a clear understanding of algorithms and techniques used in sentiment analysis
- Learn the capabilities and limitations of the applications
- Learn data handling skills
- Learn the methods where sentiment analysis can be applied
- Learn to use tidy data principles to develop text mining skills
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.
Who is your target audience?
- Analytics Professionals, modelers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand 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
- Learners should be over the age of 16, and have a basic understanding of Maths, English and ICT
- No formal prerequisites required, however, knowledge of some undergraduate level mathematics is recommended, but is not mandatory.
- Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code
- 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.
Method of Assessment
In order to complete the Sentiment Analysis course successfully, all students are required to complete a series of assignments. The completed assignments must be submitted via the online portal. Your instructor will review and evaluate your work and provide you feedback based on how well you have completed your assignments.
Upon the successful completion of the course, you will be awarded the Sentiment Analysis Certificate by CPD & iAP.
Progression and Career Path
Once you successfully complete the Sentiment Analysis course, you will gain an accredited qualification that will prove your skills and expertise in machine Learning-based approaches. With this qualification you can further expand your knowledge by studying related courses on this subject. You will also be eligible for incentives or a job promotion put forth by the organisation. Here are some job roles you can land, along with the average UK salary per annum according to https://www.payscale.com.
- Product Manager – £36,436 per annum
- Data Analyst – £26,232 per annum
- Engineer – £42,828 per annum
- Tech Executive – £19,563 per annum
- Machinist – £18,311 per annum
- Quantitative Analyst – £54,517 per annum
- Research Scientist – £30,802 per annum
- Market Researcher – £23,707 per annum
- Risk Analyst – £33,665 per annum
- Quantitative Analyst – £54,517 per annum
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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