In this world surrounded by computers and cell phones makes our day to day life so easy, the person who might be reading this could be reading it through a cell phone or a computer. So, the machine’s like computers and all have not only made our work fast, it also needs less effort for doing a particular work. So have you ever imagined who makes this machines how qualified they must be hope well trained and dedicated they must be which qualifications they must have and what type of examinations they attend and which type of questions they get so let’s get to know about what type of questions they get in their examinations to which they have to solve if no then let’s get to know about that.
So before we discuss the questions and answers let’s discuss machine learning and its benefits. Actually, machine learning is one of the fastest growing arms of the domain of Artificial intelligence Certification, it had far reaching consequences and in the next couple of years we will perceive that every industry would be using it. If we come to the benefits of machine learning or why should someone learn machine learning is machine learning might just be one of the important fields of science that we are just moving towards. It differs from other science in the scene that this is one of the domains where the input and output are not directly correlated. It is more about mimicking how humans think and solving real world problems like humans without the intervention of humans. Basically it focuses on developing computer programs that can be taught to grow and change when exposed to data.
Machine learning job profiles are becoming one of the most popular jobs all over the globe. According to LinkedIn machine learning engineer’s job profile is considered as top ten jobs across the globe and according to economic Times there were 15,000+ Jobs postings in 2019 which proves that the demand for machine learning engineer’s is at its peak.
So, let’s move on to the top interview Questions and their solution of machine learning by Sprintzeal. The first question would be what is Bias and variance? So, Bias is the difference between the average prediction of our model and the correct value and variance is the number that gives the difference of prediction over a training set and anticipated values of other training sets. Moving on to the next question that is explain false negative, false positive, true positive and true negative. When mechanical learning correctly knows the state, it is said that it has a true positive value. When the machine learning model correctly produces the negative condition or class then it is said to have a true negative value. When the mechanical learning system incorrectly identifies a negative class or condition, then it is called a false positive value. When machine learning incorrectly predicts a positive class or condition, then it is said to have a false negative value. The next question is what is the confusion Matrix? So, confusion Matrix is used to explain a model’s performance and gives the summary of predictions on the classification problems. What do you understand by type 1 and type 2 errors? Type 1 error is an error where the outcome of a test shows the non-acceptance of a true condition. Type. 2 errors are an error where the outcome of the test shows the acceptance of a false condition. What is logistic regression? Logistic regression is the proper regression analysis used when the dependent variables are categorical or binary.
Now I will like to conclude by saying that machines are increasing day by day in future, machines can play most of the roles in our day to day life that are from a coffee machine in the day, to an air conditioner in the night every machine has its individual work and values so as the engineers who work hard to make those machines be efficient. So the need of machine Engineer’s won’t decrease so everyone who is interested in machine learning should try hard for it because not only it’s one of the best jobs in the whole world but also it has a great future and can provide you a loads of reputation not only in your locality or country but in the whole world.