Supervised Machine Learning
- 2. Why it is called Supervised Learning ? • It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. • We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. • Learning stops when the algorithm achieves an acceptable level of performance
- 3. What actually happens in Supervised Machine Learning ?
- 4. What actually happens here ? • In supervised learning, we train our model on a labelled dataset ( we have raw data as well as its result ) • We split our data into a training dataset and test dataset • Training dataset is used to train our network • Testing dataset acts as new data for predicting results or to see the accuracy of our model
- 5. Supervised Machine Learning Categorisation • Classification Models • Regression Models
- 6. Classification Models • Classification models are used for problems where the output variable can be categorized, such as “Yes” or “No”, or “Pass” or “Fail”. • Classification Models are used to predict the category of the data.
- 7. Regression Models • Regression models are used for problems where the output variable is a real value such as a unique number, dollars, salary, weight or pressure, for example. • It is most often used to predict numerical values based on previous data observations.
- 8. Types : • Regression • Logistic Regression • Classification • Naïve Bayes Classifiers • DecisionTrees • SupportVector Machine
- 9. Application of Supervised Machine Learning oSentiment Analysis It is a natural language processing technique in which we analyze and categorize some meaning out of the given text data. For example, if we are analyzing tweets of people and want to predict whether a tweet is a query, complaint, suggestion, opinion or news, we will simply use sentiment analysis.
- 10. oRecommendations Every e-Commerce site or media, all of them use the recommendation system to recommend their products and new releases to their customers or users on the basis of their activities. Netflix, Amazon,Youtube, Flipkart are earning huge profits with the help of their recommendation system.
- 11. oSpam Filtration Detecting spam emails is indeed a very helpful tool, this filtration techniques can easily detect any sort of virus, malware or even harmful URLs. In recent studies, it was found that about 56.87 per cent of all emails revolving around the internet were spam in March 2017 which was a major drop fromApril 2014's 71.1 per cent spam share.