Looking To Learn Fundamental Data Science Algorithms — MachineHack Launches New Feature Called MachineHack Practise

Data Science is a field which brings computer science, statistics, mathematics and other fields together. But there is also a need for newcomers to cut through the confusion and get their hands dirty in the world of fundamental data science algorithms.

Keeping this in mind, MachineHack has launched a feature called MachineHack Practice to give young and beginner-level data scientists access to computing resource with guidance to use basic data science algorithms with the use of popular libraries. 

Please access MachineHack Practise by clicking here.

MachineHack Practise is made up of a series of tutorials which contains:

  1. Preliminary information regarding the algorithm on the home page
  2. Resources for further guidance on the home page
  3. Coding tool with guided usage of the algorithm on a small toy problem
  4. Direction to our Hackathon where you can use the knowledge to check your skills

Please access MachineHack Practise by clicking here.

The feature is designed specifically to transition young and new data scientists to tackle more challenging problems by introducing basic concepts and giving them confidence. Currently, there are 5 tutorials on the following algorithms:

  1. MachineHack Practise 1: Linear Regression
  2. MachineHack Practise 2: Multiple Linear Regression
  3. MachineHack Practise 3: Support Vector Regression
  4. MachineHack Practise 4: Decision Tree Regression
  5. MachineHack Practise 5: Random Forest Regression

Please find Machinehack Practise feature here.

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Abhijeet Katte
As a thorough data geek, most of Abhijeet's day is spent in building and writing about intelligent systems. He also has deep interests in philosophy, economics and literature.

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