Mode Of Delivery
In this video, we will see how can we apply RNN’s to classify twitter sentiment.
This video forms part of the course Machine Learning with scikit-learn and Tensorflow
Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks. We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification. The course will then take you through the methods for unsupervised learning and what to do when you have limited or no labels for your data. We use the techniques we have learned, along with some new ones, to build a sentiment classifier, an autocomplete keyboard and a topic discoverer. The course will also cover applications for Natural Language Processing, explaining the types of language processing. We will cover TensorFlow, the most popular deep learning framework, and use it to build convolutional neural networks for object recognition and segmentation. We will then discuss recurrent neural networks and build applications for sentiment classification and stock prediction. We will then show you how to process sequences of data with recurrent neural networks with applications in sentiment classification and stock price prediction. Finally, you will learn applications with deep unsupervised learning and generative models. By the end of the course, you will have mastered Machine Learning in your everyday tasks.
A go-to resource for analysts and data scientists looking forward to exploring the best of both popular frameworks - Scikit-Learn and Tensorflow.
Basic familiarity with Python is required.