Learn the text classification with the machine learning and fasttext.
Build a strong foundation in Machine Learning with this tutorial.
•Understanding of text classification
•Leverage Machine Learning to classify text
•Use fastText for training and prediction
A Powerful Skill at Your Fingertips Learning the fundamentals of text classification puts a powerful and very useful tool at your fingertips. fastText is free, easy to learn, has excellent documentation.
Jobs in machine learning area are plentiful, and being able to learn document classification with machine learning will give you a strong edge.
Machine Learning is becoming very popular. Alexa, Siri, IBM Deep Blue and Watson are some famous example of Machine Learning application. Document classification is vital in information retrieval, sentiment analysis and document annotation. Learning document classification with machine learning will help you become a machine learning developer which is in high demand.
Big companies like Google, Facebook, Microsoft, AirBnB and Linked In already using text classification with machine learning in information retrieval, content ranking, sentiment analysis and ad targeting in social platforms. They claimed that using Machine Learning and text classification has boosted productivity of entire company significantly.
Content and Overview
This course teaches you on how to build document classification using open source fastText framework. You will work along with me step by step to build following answers
Introduction to text classification.
Introduction to Machine Learning
Training fastText model using cooking recipe dataset
Tune the accuracy of model
Learn variation of model
Learn use cases of fasttext
What am I going to get from this course?
•Learn text classification with fasttext and Machine Learning programming from professional trainer from your own desk.
•Over 10 lectures teaching you document classification programming
•Suitable for beginner programmers and ideal for users who learn faster when shown.
•Visual training method, offering users increased retention and accelerated learning.
•Breaks even the most complex applications down into simplistic steps.
•Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.