Data continues to inhabit every facet of human existence and so the need for competent Data Scientists to help leverage the insights from that data will invariably increase for the foreseeable future. According to a past EMC Data Scientist Study and the 2015 Global IT Report, the amounts of data created by the year 2020 will be upwards to 44 times what they were in 2009. Data Scientists use Machine Learning (ML) skills to develop powerful algorithms to make sense of the avalanche of data. Thus, Data Scientists with superior Machine Learning skills will be the transformative heroes of the digital world. Machine Learning teaches computers to conduct particular tasks like pattern diagnosis and recognition, planning, or prediction without the presence of any programming control ML generates "algorithms" that turn into self-teaching entities when exposed to data.
This is just the beginning. Technology, which promises to bring huge changes to the world in coming years, is nothing but Machine Learning. It is an essential part of Artificial Intelligence research and gained the highest limelight in business. Due to the wide usage of digital devices, Machine Learning has offered a revolutionary way of solving tasks which can be data analysis, classification, forecasting, image recognition, etc.
All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them.