Sentiment Analysis or Opinion Mining, is a form of Neuro-linguistic Programming which consists of extracting subjective information, like positive/negative, like/dislike, and emotional reactions. This Machine Learning – Twitter Sentiment Analysis in Python course uses real examples of sentiment analysis, so learners can understand it’s important, and how to use it to solve problems. During the course learners will undertake a project on Twitter sentiment analysis, and will understand all the fundamental elements of sentiment analysis in Python.
Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.
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.
Those who successfully pass this course will be awarded a Machine Learning – Twitter Sentiment Analysis in Python certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.
Once you have completed this Machine Learning – Twitter Sentiment Analysis in Python course you will have desirable skills. You could go on to further study of machine learning and Python, or could gain entry level employment in this area. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £38,455 (payscale.com). When you complete this Machine Learning – Twitter Sentiment Analysis in Python, you could fulfil any of the following roles:
|1: What are You Feeling Like?|
|1. Introduction: You, This Course & Us!|
|2. Sentiment Analysis: What’s all the fuss about?|
|3. Machine Learning Solutions for Sentiment Analysis: the devil is in the details|
|4. Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)|
|5. Installing Python – Anaconda and Pip|
|6. Back to Basics: Numpy in Python|
|7. Back to Basics: Numpy & Scipy in Python|
|8. Regular Expressions|
|9. Regular Expressions in Python|
|10. Put it to work: Twitter Sentiment Analysis|
|11. Twitter Sentiment Analysis: Work the API|
|12. Twitter Sentiment Analysis: Regular Expressions for Preprocessing|
|13. Twitter Sentiment Analysis: Naive Bayes, SVM & SentiWordNet|