In this lesson, we have discussed unsupervised learning from a theoretical and practical perspective. We have seen how we can make use of predictive analytics and find out how we can take advantage of it to cluster records belonging to a certain group or class for a dataset of unsupervised observations. We have discussed unsupervised learning and clustering using K-means. In addition, we have seen how we can fine tune the clustering using the Elbow method for better predictive accuracy. We have also seen how to predict neighborhoods using K-means, and then, we have seen another example of clustering audio clips based on their audio features. Finally, we have seen how we can use unsupervised kNN for predicting the nearest neighbors.

In the next lesson, we will discuss the wonderful field of text analytics using TensorFlow. Text analytics is a wide area in natural language processing (NLP), and ML is useful in many use cases, such as sentiment analysis, chatbots, email spam detection, text mining, and natural language processing. You will learn how to use TensorFlow for text analytics with a focus on use cases of text classification from the unstructured spam prediction and movie review dataset. Based on the spam filtering dataset, we will develop predictive models using a LR algorithm with TensorFlow