Last Updated on July 31, 2020 Randomness is a big part of machine learning. Randomness is used as a tool or a feature in preparing data and in learning algorithms that map input data to output data in order to make predictions. In order to understand the need for statistical methods in machine learning, you must understand the source of randomness in machine learning. The source of randomness in machine learning is a mathematical trick called a pseudorandom number generator. […]

Last Updated on August 20, 2020 There may be complex and unknown relationships between the variables in your dataset. It is important to discover and quantify the degree to which variables in your dataset are dependent upon each other. This knowledge can help you better prepare your data to meet the expectations of machine learning algorithms, such as linear regression, whose performance will degrade with the presence of these interdependencies. In this tutorial, you will discover that correlation is the […]

Last Updated on August 8, 2019 A sample of data is a snapshot from a broader population of all possible observations that could be taken of a domain or generated by a process. Interestingly, many observations fit a common pattern or distribution called the normal distribution, or more formally, the Gaussian distribution. A lot is known about the Gaussian distribution, and as such, there are whole sub-fields of statistics and statistical methods that can be used with Gaussian data. In […]

Last Updated on August 8, 2019 We have an intuition that more observations is better. This is the same intuition behind the idea that if we collect more data, our sample of data will be more representative of the problem domain. There is a theorem in statistics and probability that supports this intuition that is a pillar of both of these fields and has important implications in applied machine learning. The name of this theorem is the law of large […]