Machine Learning is a crucial part of AI and allows computers to run on self-learning mode without explicit programming. These computers are able to learn, adapt, and develop by themselves through the data they are fed. The concept is not new, but its ability to quickly and automatically carry out mathematical calculations on big data is now gaining traction.
Data Science needs the application of Machine Learning due to its high-value predictions that can drive better decision-making in real-time without the need for human intervention. It helps analyze data of massive sizes and makes the job of Data Scientists easy with its automation.
Traditional statistical methods have been replaced by Machine Learning techniques that introduce automated sets of the typical methods and this has changed the way for data extraction and interpretation. That being said, without data, it will be all rendered useless. There will be nothing to learn from. Machine Learning is only good as long as it receives data and the algorithms consume it. So, Data Science and Machine Learning have to work in conjunction to be of substantial value.
Data Science lacks the reasoning behind why things work and the capability to solve non-standard problems, which is what Machine Learning aids in.
Learn more about the Machine Learning algorithms for Data Science.
Here is another video that explains the Machine Learning algorithms
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