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1. What Is Machine Learning?

In the world of regular machines, humans are needed to operate them manually. Some intelligent machines or computers need our instructions before they can function and work. In the world of humans, we choose what we want to do even if we are being guided. Humans are capable of learning anything and they learn more from their experiences in life or with their dealings with others and situations.  This fact has made many to wonder if a machine can learn from the experiences or data it has gathered from the past. So, what actually is Machine Learning?  Machine Learning is clearly a subsection of artificial intelligence that deals with the development of algorithms which enable a computer to learn on its own using data and experiences gathered from the past. The term Machine Learning was first used in 1959.  When a computer or machine is able to learn from its past experiences, it can help improve its functionality and performances. Even when it is not programme
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Python If-else statements Decision making is the most important aspect of almost all the programming languages. As the name implies, decision making allows us to run a particular block of code for a particular decision. Here, the decisions are made on the validity of the particular conditions. Condition checking is the backbone of decision making. In python, decision making is performed by the following statements. Statement Description If Statement The if statement is used to test a specific condition. If the condition is true, a block of code (if-block) will be executed. If - else Statement The if-else statement is similar to if statement except the fact that, it also provides the block of the code for the false case of the condition to be checked. If the condition provided in the if statement is false, then the else statement will be executed. Nested if Statement Nested if statements enable us to use if ? else statement inside an outer if statement. Indentation in Py

Tutorial 5 of Stage 2: What is Backward Elimination?

Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output. There are various ways to build a model in Machine Learning, which are: All-in Backward Elimination Forward Selection Bidirectional Elimination Score Comparison Above are the possible methods for building the model in Machine learning, but we will only use here the Backward Elimination process as it is the fastest method. Steps of Backward Elimination Below are some main steps which are used to apply backward elimination process: Step-1:   Firstly, We need to select a significance level to stay in the model. (SL=0.05) Step-2:   Fit the complete model with all possible predictors/independent variables. Step-3:   Choose the predictor which has the highest P-value, such that. If P-value >SL, go to step 4. Else Finish, and Our model is ready.