Many people still do not understand the differences between machine learning and statistics. Some believe that machine learning is just an overhyped form of statistic rebranded in the age of advanced computing and big data. Others believe that both these topics are completely different from one another. Read on to learn more about statistics and machine learning using python. This article also discusses how both the subjects are interrelated.
What is statistics?
Statistics is defined as a branch of science that deals with the development and studies of various analyses, interpretation, data collection, and presentation of empirical data.
For thousands of users, statistics have been used for the evaluation and collection of information. Statistics involves two distinctive methods inferential statistics and descriptive statistics.
Descriptive statistics is the process for summarising information of a sample with the help of metrics like mean, mode, median, and standard deviation. It also includes exploratory data analysis for managing large projects. Similarly, descriptive statistics are used in various stages of an investigation.
Inferential statistics helps in understanding the inferring properties of an entity based on properties of the sample class.
What is machine learning?
Machine learning is a field of advanced computing. It deals with algorithm creation for the functioning of systems and programs. Machine learning is facilitating tasks like sentiment analysis and text mining.
Machine learning using python comprises three different methodologies and unsupervised learning, supervised learning, and reinforcement learning. Supervised learning involves the target outcome variable. Whereas in unsupervised learning, there is no target outcome algorithms work to find patterns and relationships between data. Reinforcement learning makes use of a trial and error technique to reach the outcome.
Machine learning is a new concept that has come to light over the last two decades. The exponential growth of data processing and collection has created a huge demand for machine learning technology.
Relationship between statistics and machine learning
Various techniques of machine learning are derived from statistics. Functions like logistic regression and linear regression are an integral part of machine learning methodologies. However, modern machine learning techniques mostly involve coding. Hence engineers and Modern-day engineers are equipped with libraries for creating instructions for machine learning. They often argue about the fact that understanding statistics is not necessary for machine learning. But for advanced applications of machine learning, data scientists draw their knowledge from statistics and probability.
Differences between machine learning and statistics
Machine learning makes use of the bulk volume of data to make accurate predictions. Statistics do not require multiple data subsets for predictions. It essentially shows the relationship between the data and the outcome.
The purpose of statistics and machine learning is not the same. Statistics helps in creating inference machine learning is used for repeated predictions. Machine learning using python helps in finding patterns and relations within a large set of data.
Bulk volumes of data in machine learning can make accurate predictions that are very difficult to understand. Understanding statistical models are relatively easier as it involves fewer variable.
Both these disciplines have different purposes and hence are not replacements for one another. The use of a machine learning model or statistical model is completely dependent on the outcome. Data scientists solve problems using bulk volumes of data. Statistics help in developing appropriate models in machine learning.