Sentiment Analysis using fastText and Machine Learning

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Learn Sentiment Analysis with the machine learning and fasttext. Build a strong foundation in Machine Learning with this tutorial. Understanding of Sentiment Analysis Leverage Machine Learning to train and predict sentiments of reviews Use fast Text for training word vectors Use fast Text word embeddings for sentiment analysis A Powerful Skill at Your Fingertips  Learning the fundamentals of sentiment analysis puts a powerful and very useful tool at your fingertips. fast Text is free, easy to learn, has excellent documentation. Jobs in machine learning area are plentiful, and being able to learn sentiment analysis 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. Understanding word embeddings is vital in information retrieval, sentiment analysis and document annotation.  Learning word embeddings with machine learning will help you become a machine learning developer which is in high demand. Big companies like Google, Facebook, Microsoft, Air Bn B and Linked In already using word embeddings with machine learning in information retrieval, content ranking, sentiment analysis and ad targeting in social platforms. They claimed that using Machine Learning and word embeddings has boosted productivity of entire company significantly. Content and Overview  This course teaches you on how to build sentiment analysis using open source fast Text framework.  You will work along with me step by step to build following answers Introduction to sentiment analysisfast Text deepdive Training fast Text model Tune the accuracy of  model Learn use cases of fasttext What am I going to get from this course?Learn sentiment analysis with fasttext and Machine Learning programming from professional trainer from your own desk. Over 10 lectures teaching you word embeddings 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.