People have always been fascinated by the idea of giving human-like characteristics to inanimate objects. A major component of this over the last century has been the focus on creating “thinking machines.” Advancements in programming and processing have introduced concepts such as machine learning and deep learning into our vocabularies. However, although these terms may be used interchangeably by some, they are different concepts. It might help to think of these ideas as subsections of concentric circles. For starters, artificial intelligence refers to anything that can perform an intellectual task usually performed by a human. The most basic form of this is “if/then” programming. If a computer encounters a specific parameter in its program, the program tells it what to do next. Machine learning, on the other hand, means systems can learn to perform tasks by themselves without human intervention through continued exposure to data. Some examples of this principle in action include weather forecasting and antivirus software. The more information these are exposed to, the more accurate their predictions and conclusions will be. Worth noting? There’s also a largely silent partner in this programming progression: Python. Interested in learning more about why Python is the best choice for these machine learning capabilities? Be sure to continue reading on to the resource supported alongside this post for more information.
Python Programming & Its Importance In Machine Learning provided by Accelebrate, a company offering courses in Python Data Science training
