How To Practice Machine Learning?

If you are a beginner in Machine Learning you must be confused about how to use which algorithm where and how to choose whether we need to train an algorithm or we need to build a Neural Network. Well, my suggestion to you about this problem is that you will learn everything with time and experience. So do you need to get this experience by doing internships or working under a mentor only? No, The best way to get experienced with Machine Learning is to practice Machine Learning.

So, here in this article, I will take you through the best ways of how you can practice Machine Learning.

Also, Read – Machine Learning Interview Questions.

Practice Machine Learning with Projects

The best way to practice machine learning is by working on projects. The idea is that whenever you come up with any new concept in machine learning the next thing to do after learning that concept is to search for a project based on that topic. For example – let’s say I learnt about Precision and Recall, which is a method to evaluate the performance of a machine learning model. Now precision and recall contribute a very small amount of code while working on a project, but you need to find a complete article based on precision and recall only.

This way you can practice every topic you come across while working on projects because you will always see different projects with different explanations. You can find plenty of Projects to practice machine learning from here.

Work with Different Data

Don’t stick to the same format of data try to use different datasets. Data has also different types, although data is either structured or unstructured by those types has also different categories. 

The importance of using different data is that business decisions should be made based on constantly changing data from various sources. Your data sources can include both traditional systems of record data (such as customer, product, transactional, and financial data) and external data (for example, social media, news, weather data, image data or geospatial data). Also, many data structures are essential for analyzing information, including structured and unstructured data.

Types of Data to Practice Machine Learning

The types of data you must be experienced with are:

  • Sensor data: Examples include radio frequency identification (RFID) tags, smart meters, medical devices, and global positioning system (GPS) data.
  • Blog data: when servers, applications, networks, etc. work, they capture all kinds of data about their activity.
  • Point of Sale Data: When the cashier swipes the barcode of any product you purchase, all data associated with the product is generated.
  • Financial data: Many financial systems are now programmatic; they operate according to predefined rules that automate the processes.
  • Weather data: Sensors to collect weather data are deployed in towns, cities and regions to collect data on things like temperature, wind, barometric pressure and precipitation. This data can help meteorologists create hyperlocal forecasts.
  • Click Flow Data: Data is generated every time you click a link on a website. This data can be analyzed to determine customer behaviour and purchasing patterns.

When you will practice machine learning by working on these different types of data you will get to work on a lot of different situations. You can get any type of data on the internet easily. But if you faced any problem in collecting data then you can just contact me at –

Machine Learning is all dependent on data, and experience will come by working on projects so working on different types of data and working on Projects are the best ways to practice machine learning.

Also, Read – Galaxies Classification Model with Machine Learning.

I hope you liked this article on how to practice machine learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.

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Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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