Machine Learning Applications Made Possible Through Python
As Artificial Intelligence continues to evolve, the means in which we can apply it into our every day lives do as well. One subset of AI in particular has seen significant growth over the past few years: Machine Learning. This AI allows computers to learn through the analyzation and interpretation of data without any explicit programmer intervention or code. The analysis and interpretation done allows companies around the world to learn and identify different possible solutions for the many complex problems they face on a daily basis.
Machine Learning has also made way for advancements that most individuals see on a daily basis, but hadn’t even considered were a result of a form of AI. For example, Instagram users will be shown recommended content or accounts to follow based on their account’s activity and followed users. This can also been seen through Facebook’s suggestion of recommended friends based on similar connections that you and the suggested friend might have. Or in the case of Amazon users, you may receive product recommendations on a regular basis based on your most recent purchases. Believe it or not, there are also even more complicated services offered as a result of Machine Learning. For example, automated fraud detection from your banking provider is made possible through Machine Learning. Similarly, translation services, and even predictive text finishers on your phone are a result of python-powered machine learning applications.
Now, if you’re wondering how all of these different applications are made possible, you’re probably not alone. A majority of these possibilities first require massive amounts of data input to be assessed. Once the data has been assed, programmers develop the code that breaks down the data into more meaningful and interpretable insights that companies are then able to use to develop business strategies in real-time. The coding language that makes all of this possible is Python.
Python is the preferred language of choice for a number of these Machine Learning and Data Science applications due to its ability to fluently work with large amounts of data. In addition to this ability, Python possess a straightforward syntax, making it easier to pick up on for the inexperienced. For those that prefer working on a different operating system or with a different programming language when necessary, Python is particularly flexible and compatible, making it the perfect choice for these situations. If that weren’t enough, due to its open-source nature, there are also an extensive amount of online libraries that include large amounts of pre-written code able to be integrated into any project.
These advantages are a large part of why Python is the most common choice for programmers working with Machine Learning and Data Science applications and projects. For more information on how Artificial Intelligence is shaped by Python, consider reviewing the infographic below. Python Programming & Its Importance In Machine Learning will provide great insight into the many additional benefits that Python provides.
Author Bio: Anne Fernandez – Anne joined Accelebrate in January 2010 to manage trainers, write content for the website, implement SEO, and manage Accelebrate’s digital marking initiatives. In addition, she helps to recruit trainers for Accelebrate’s Python Training courses and works on various projects to promote the business.
