All machine learning courses in India need proficiency in statistics. However ML is not only statistics but definitely draws inspiration from analysis of statistics. This is so because data is their common factor. An ML-engineer though must and should have proficiency in statistics, while an ML-expert needs to only have sufficient knowledge of basic statistical techniques and data management. Let’s look into why this is so.

**Overlaps of Machine Learning and Statistics**

**Machine learning courses** of today borrow concepts like data analysis and statistical modelling to arrive at predictive models for ML. Machine Learning is a branch of computer science while statistics deals with the analysis of statistics in pure mathematics. However, they are interdependent mathematical applications both dealing with the analysis of data, data models, and problem-solving.

It goes without saying that statistics is the older sibling and yet today even statisticians use ML to achieve its end results with Big Data and for Predictive Analysis. Similarly, ML draws on statistical analysis though its aim is entirely different. That’s why **Big Data Hadoop training courses** also need knowledge of statistics and database management.

Mostly the overlap and confusion occur because both use algorithms and data to predict the end results. However, it is incorrect to equate the two, which are separate advanced fields, in two different branches. They are at best complementary interdependent fields which can aid each other much like siblings often do. Two separate individuals, completely different, in one environment, and with individual destinations. Sure they walk the same path at times!

**Clearing the Confusion**

Statistics uses a model with defined parameters fitting the data tested through classification and regression techniques to account for clustering and density estimation, to provide the best inference. ML works with networks, graphs and bar charts learning from general data through assigned weights using unsupervised learning techniques to give an accurate prediction of outcomes.

Looking very closely into the two one will notice that ML has no set rules, equations, parameters, variables or assumptions. It learns from the data input and provides a predictive outcome. In statistics, you get an inference unique to a small data set with fixed variables and based on strict regression and classification techniques of mathematical equations. Though older, statistics is pure math. ML is a carefree youngster, which uses and learns from past data, has no limit to data used or variables present and works with algorithms that govern data to give an accurate predictive outcome.

An ML Engineer and Statistician may have areas where their jobs overlap. They share a common path through the use of modelling and data and then branch out to their own destinations. Truly they are complementary in nature bring out the best in the other and helping each other achieve individual end results.