Here in this post, I’ll try to clarify what do these big terms like Data Science, Machine learning, Artificial Intelligence and Deep Learning in reality mean and also I will try to differentiate them so that you can understand better.
Data science is an interdisciplinary field which is a combination of programming, analytical, and business skills that allow extracting meaningful insights from both structured and unstructured data. Data science is a broad term which includes data analyst, data engineer and data architect. With Data Science, you can Clean, munge data and make it ready for processing and analysis and for deploying statistical, machine learning and analytical methods. It also helps us to see various angles to determine hidden patterns, relations and trends.
Artificial Intelligence :
Artificial Intelligence aka AI sometimes called machine intelligence, as defined by John McCarthy, who coined the term in 1956, defined it as “The science and engineering of making intelligent machines, especially intelligent computer programs”. Basically in Artificial Intelligence computers or machines can be trained to accomplish human-like tasks by processing large amounts of data by recognizing patterns in them.
AI examples that you hear about today ranging from the virtual assistant that comes in your phone including SIRI, Cortana, Google assistant to Google Search algorithm to self-driving cars.
Deep learning is a type of machine learning. Both Deep Learning and Machine Learning fall under Artificial Intelligence. The basic difference between machine learning and Deep learning is that Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned but still needs some guidance whereas Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. The output of a deep learning can be anything from a score or text or an audio.
Machine learning is a method of data analysis that helps you build the analytical models. These model learns from data, identify patterns and helps business makes better decisions. Basically, the output of these model is a numerical value like a score or a classification. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
All of these technologies have been surprising us each day with its capabilities to do wonders, and this trend will continue in the future as well.
Do let us know what do you think.