In Data Science, machine learning algorithms have captured the spotlight. These algorithms thrive on enormous datasets and keep evolving as time passes, constantly fine-tuning themselves. As they gather more and more data over time, they naturally become more automated and efficient. One use of these machine learning algorithms is in creating artificial intelligence.
Successful Implementation Steps:
- The first step in making a successful algorithm is to collect large quantities of data.
- Then, a group of software engineers and data scientists work together to develop an algorithm to utilize the data to become better.
- Afterward, the algorithm is rigorously tested, and a beta version is sent out to developers to test.
- Finally, the final product is published for users to use, or a paid version is released to be sold.
- A benefit of the algorithms is that they help automate tasks that otherwise would take large groups of data scientists to maintain.
- The models are self-learning, and as they get more data, they resolve issues efficiently.
- The algorithms can be used in multiple fields and are flexible in implementation.
- Cheap to maintain in the long run.
The graphic above describes the different types of machine-learning algorithms. It shows algorithms used for particular types of work. Unique types of algorithms exist to carry out individual tasks, and each requires different speeds for the algorithm to function.
As of right now, I am working on a personal project in which I am using a regression model to prove why three-point shots are statistically worth more than two-point shots in basketball. My first course of action was to gather all the raw data that I could. I went to a website called Basketball Reference and downloaded the past 50 years of shooting data. Afterward, I constructed an algorithm using code. In the end, I was able to prove my hypothesis.