Question-1: In Common term what do you mean by Advanced Analytics?

Answer: From 10,000 feed view we can say that Advanced Analytics is a collection of techniques which you are using to solve the core problem of deriving insights and making predictions or recommendations based on the data.

Question-2: Can you give some examples which you perform as part of Machine Learning?

Answer: There 1000 different types of tasks you can perform using Machine Learning but below are the few most common task which you usually performs.

• Supervised Learning: Based on the data characteristics or features you try to predict a label for each given data point. And that is also part of your supervised learning.
• Recommendations: recommending users new products based on his or her past behavior.
• Unsupervised Learning: You use various different techniques to discover the structure in the data.
• Graph Analytics: This quite a different and wide domain in itself. Which is used to find or search the patterns in social networking.

Question-3: Can you explain what is a supervised learning?

Answer: - Supervised Learning is most commonly used Machine Learning. In this you have already have historical data with the labels and features. You would create a Machine Learning model such that using features of new data you predict the labels. For example, lets assume you have to predict salary for each individual based on his/her characteristics.

Here, label would be salary and features are his or her age, years of experience, gender etc. You already have some historical data with this information and you need to predict salary for new data.

Question-4: What do you mean by dependent variable in the Supervised Machine Learning?

Answer: When you run Machine Learning Models for supervised learning and expect some output as part of the Machine Learning model completion which is usually some predictions. Like based on the number of years of experience, Age and Gender your model predict salary. This predicted label is called dependent variable. Conceptually, you can think of this label (salary) is depend on the features.

Question-5: What are the independent variables in supervised learning?

Answer: In Supervised Machine Learning you need to predict (dependent variable) based on the independent variable like age, gender, experience etc. And these independent variables are also referred as Features. Independent variables are the input to your Machine Learning model which would be analyzed to generate output (independent variables or labels).