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Most Important 40 Machine Learning Interview Questions

 Machine Learning Interview Questions: . We’ve traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You’ll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you’ll be asked about what’s going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward. We’ve divided this guide to machine learning interview questions into the categories we mentioned above so that you can more eas
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Importance of Data Science

  Data is one of the important features of every organization because it helps business leaders to make decisions based on facts, statistical numbers, and trends. Due to this growing scope of data, data science came into picture which is a multidisciplinary field. It uses scientific approaches, procedure, algorithms, and framework to extract the knowledge and insight from a huge amount of data. The extracted data can be either structured or unstructured. Data science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data. Data science is an extension of various data analysis fields such as data mining, statistics, predictive analysis and many more. Data Science is a huge field that uses a lot of methods and concepts which belongs to other fields like information science, statistics, mathematics, and computer science. Some of the techniques utilized in Data Science encompasses machine l

What is a Type II Error?

  A Type II error is a false negative in a test outcome, where something is falsely inferred to not exist. This usually means incorrectly accepting the null hypothesis (H0), which is the testing statement that whatever is being studied has no statistically significant effect on the problem. An example would be a drug trial that incorrectly concludes the prescribed medication had no effect on the patient’s ailment, when in fact the disease was cured, but subsequent exams caused a false positive showing the patient was still sick. Null Hypothesis and Statistical Significance In practice, the difference between a false positive and a false negative is usually not so clear-cut. Since the tests are most often quantitively rather than qualitatively based, the results tend to be expressed in a confidence interval value less than 100%, rather than a simple Yes/No decision. This question of how likely the results are to be found if the null hypothesis is true is called statistical significance.

How to reverse a String in Java ?

A Traditional  for   Loop  We know that strings are immutable in Java. An immutable object is an object whose  internal state remains constant  after it has been entirely created. Therefore, we cannot reverse a  String  by modifying it. We need to create another  String  for this reason. First, let's see a basic example using a  for  a loop. We're going to iterate over the  String  input from the last to the first element and concatenate every character into a new  String : public String reverse (String input) { if (input == null ) { return input; } String output = "" ; for ( int i = input.length() - 1 ; i >= 0 ; i--) { output = output + input.charAt(i); } return output; } As we can see, we need to be careful at the corner cases and treat them separately. Using A   StringBuilder Java also offers some mechanisms like  StringBuilder  and  StringBuffer  that create a mutable sequence of characters . These objects have

The Art of Winning Kaggle Competitions

 What is Kaggle? Learning data science can be overwhelming. Finding a community to share code, data, and ideas can se e m also seem like an overwhelming as well as farfetched task. But, there is one spot where all of these characteristics come together. That place is called Kaggle. Looked at more comprehensively, Kaggle is an online community for data scientists that offers machine learning competitions, datasets, notebooks, access to training accelerators, and education. Anthony Goldbloom (CEO) and Ben Hamner (CTO) founded Kaggle in 2010, and Google acquired the company in 2017. Kaggle competitions have improved the state of the machine learning art in several areas. One is mapping dark matter; another is HIV/AIDS research. Looking at the winners of Kaggle competitions, you’ll see lots of XGBoost models, some Random Forest models, and a few deep neural networks. The Winning Recipe of Kaggle Competitions involves the following steps: Step one   is to start by reading the competition gu

Tree Data Structure and Its all types

  What are tree Data Structures? Tree is a hierarchical data structure that stores the information naturally in the form of a hierarchy style. Tree is one of the most powerful and advanced data structures. It is a non-linear data structure compared to arrays, linked lists, stack, and queue. It represents the nodes connected by edges. The above figure represents the structure of a tree. Tree has 2 subtrees. A is a parent of B and C. B is called a child of A and also a parent of D, E, F. Tree is a collection of elements called nodes, where each node can have an arbitrary number of children. Field Description Root Root is a special node in a tree. The entire tree is referenced through it. It does not have a parent. Parent Node Parent node is an immediate predecessor of a node. Child Node All immediate successors of a node are its children. Siblings Nodes with the same parent are called Siblings. Path Path is a number of successive edges from source node to destination node. Height of Node