Question-11: Which are major categories in which you can divide Machine Learning?

Answer: Machine Learning can be classified in three categories as below.

• Supervised Machine Learning.
• Unsupervised Machine Learning.
• Reinforcement Machine Learning.

Question-12: What do you mean by reinforcement learning in Layman words?

Answer: In Layman words you can say, Reinforcement Learning is “Learn by Mistakes”.

Question-13: Which type of Machine Learning is relative easier to implement among all three?

Answer: Supervised Machine Learning is relatively easier to implement as well as understand. You can think like you are teaching very young children with the flashcards. Flash cards has labels (dependent variables) and some features in the form of picture (that is independent variables).

Question-14: Again, why multiple iteration during Machine Learning Model Training?

Answer: When you train your machine Learning Model, you keep feeding new data and check it has predicted correct label or not. If not, you adjust your algorithms, and again feed the data and check its predicted value. Over time, the algorithm would learn to approximate the exact nature of relationship between input labels and its features (dependent variable) or characteristics (independent variables).

Question-15: What happened when your Supervised Machine Learning Algorithm is fully trained?

Answer: Once, your Machine Learning algorithm is fully trained then you can productionize it and would be able to observe new and never before seen data and predict almost correct label for that new data.