Machine Learning Interview Questions: 1. What is Machine Learning (ML)? We will begin with the most informal and simple definition. Machine learning is a thing-labeler where you explain your task with examples instead of instructions. The concept behind Machine Learning is that you feed data to machines and let them learn on their own without any human intervention (in the process of learning). Consider a small scenario. Let’s say that you have enrolled for some swimming classes and you have no prior experience of swimming. Needless to say that initially, you would perform not so well because you have no idea about how to swim, but as you observe and pick up more information, your performance keeps getting better. The observance is just another way of data collection. Just like how we humans learn from our observations and experiences, machines are also capable of learning on their own when they are fed a good amount of data. This is precisely how we employ the concept of Machine Learn...
Some important topics in Statistics: - Central Limit Theorem - Mean CLT Statement: For large sample sizes, the sampling distribution of means will approximate to normal distribution even if the population distribution is not normal. If we have a population with mean μ and standard deviation σ and take large random samples (n ≥ 30) from the population with replacement, then the distribution of the sample means will be approximately normally distributed. Why CLT is important? The field of statistics is based on fact that it is highly impossible to collect the data of entire population. Instead of doing that we can gather a subset of data from a population and use the statistics of that sample to draw conclusions about the population. In practice, the unexpected appearance of normal distribution from a population distribution is skewed (even heavily skewed). Many practices in statistics such as hypothesis testing make this assumption that the population on which they work is normall...