Machine Learning

  ML: Intro to Machine Learning Q1.  Applications of the Supervised Learning Select the problem statements where you can apply supervised algorithms. 1.      For an e-commerce website, segmenting the unlabelled customers based on their behaviour from a large dataset. 2.      Given data on crop yields over the last 50 years, trying to predict next year's crop yields. 3.      Based on data samples of webpages, classifying a webpage whether the content on the web page should be considered "child friendly" or "adult". 4.      Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different groups of such patients.   Ans: Correct Answer: Given data on crop yields over the last 50 years, trying to predict next year’s crop yields. Based on data samples of webpages, classifying a webpage whether the content on the web page should be considered “child friendly” or “adult”

 Supervised Learning  System Machine Learning systems can be classified according to the amount and type of supervision they get during training . There are four major categories:        i.           supervised learning      ii.           unsupervised learning    iii.           semi supervised learning    iv.           Reinforcement learning   Supervised learning In supervised learning , the training set you feed to the algorithm includes the desired solutions, called labels (Figure 1). Figure 1. A labeled training set for spam classification (an example of supervised learning) A typical supervised learning task is classification. The spam filter is a good example of this: it is trained with many example emails along with their class ( spam or ham), and it must learn how to classify new emails. Another typical task is to predict a target numeric value, such as the price of a car, given a set of features (mileage, age, brand, etc.) called predictors . Thi