Well, there is no straightforward and sure-shot answer to this question. The answer depends on many factors like the problem statement and the kind of output you want, type and size of the data, the available computational time, number of features and observations in the data, to name a few. It is usually recommended to gather a good amount of data to get reliable predictions. However, many a time the availability of data is a constraint. So, if the training data is smaller or if the dataset has a fewer number of observations and a higher number of features like genetics or textual data, choose algorithms with high bias/low variance like Linear regression, Naïve Bayes, Linear SVM.
Logistic Regression is likely the most commonly used algorithm for solving all classification problems. It is also one of the first methods people get their hands dirty on. We saw the same spirit on the test we designed to assess people on Logistic Regression. More than 800 people took this test. This skill test is specially designed for you to test your knowledge on logistic regression and its nuances.
These questions can make you think THRICE! Machine learning and data science are being looked as the drivers of the next industrial revolution happening in the world today. This also means that there are numerous exciting startups looking for data scientists. What could be a better start for your aspiring career! However, still, getting into these roles is not easy. You obviously need to get excited about the idea, team and the vision of the company. You might also find some real difficult techincal questions on your way. The set of questions asked depend on what does the startup do. Do they build ML products? You should always find this out prior to beginning your interview preparation. To help you prepare for your next interview, I've prepared a list of 40 plausible & tricky questions which are likely to come across your way in interviews. If you can answer and understand these question, rest assured, you will give a tough fight in your job interview. Note: A key to answer these questions is to have concrete practical understanding on ML and related statistical concepts. You can get that know-how in our course'Introduction to Data Science'!