I am sharing some brief but insightful videos that explain the essential Machine Learning (ML) algorithms quite well. All these videos are part of Data Science and Machine Learning Essentials course by Microsoft on edX platform.
If you’re interested in learning Machine Learning thoroughly, I would highly recommend longer Machine Learning course by Stanford University professor Andrew Ng on Coursera platform. It is one of the best CS courses I have ever taken!
Watch these wonderful videos –
- Classification – In classification we try to predict if the given test entity belongs to a specific class or not based on the training set we use to train the algorithm. Thus, classification is predicting a true/false value for an entity with a given set of features. For example, we use classification to determine if the given email is a SPAM or not. The mail is checked for various features such as presence of certain words in its contents, the sender etc. to determine f it can be classified as a SPAM or not. It can also be used to detect credit card frauds, detecting if tumour is malignant or not and many such classification problems.
- Regression – Regression is used to predict a real numeric value outcomes. It can be used to predict sales figures, number of customers for the business based on the training set we use to train the algorithm. The training set examples contain features that denote factors that are most likely to have effect on the outcome. For example, to predict selling price of the house, its total built-up area would be one of the most important features.
- Clustering – Unlike Classification and Regression, clustering is an unsupervised ML algorithm. In clustering, we try to group entities with similar features. For example,clustering can be used to determine the locations of telephone towers so that all users receive optimum signals. We may also use clustering to group products or customers where we may not have established categorization.
- Recommendation – Recommendation is used to recommend an item to a user based on his previous usage/purchases or preferences of similar users. For example, it can be used on online shopping sites such as Amazon to recommend new books or items to a user. Netflix uses it to recommend movies to their customers.