Most people are familiar with another expression of machine learning’s more famous cousin, AI or Artificial Intelligence. This term is generally used to refer to the use of algorithms and data to mimic human learning, which results in continually improved accuracy within a system. Now that we have covered the basics, you may be wondering what is machine learning used for?
An excellent example of machine learning implementation is your Netflix recommendations. Netflix’s machine learning algorithm collects data each time you choose a movie or show and uses that data to create targeted suggestions. This queue may contain sequels, alternative media within your most liked genre, and content involving actors and actresses you have enjoyed in the past.
Machine Learning in Data Science
One of the most critical components of data science is machine learning. Machine learning algorithms are based on statistical and mathematical methods. Over time, training data obtained through unsupervised learning helps teach the system how to make accurate predictions or categorizations. These insights are created through reinforcement learning, similar to how humans process new information based on what they have already learned through past experiences.
Deep Learning vs. Machine Learning
The terms “deep learning” and “machine learning” are often used interchangeably, but it is vital to recognize the differences if you want to truly understand machine learning techniques and processes. These differences lie within specific algorithms. Deep learning tends to automate the feature extraction portion of the process. This automation cuts back on the amount of human intervention required to ensure success in using larger data sets.
Conversely, machine learning is somewhat dependent on human interaction. Data scientists determine the set of features that distinguish data categories. This intervention aids the system in understanding data input differences and therefore requires the machine to learn from more structured data.
Supervised Machine Learning Techniques
An example of supervised machine learning is your email’s spam folder. In a supervised learning environment, input data is fed into the model and then fitted appropriately through a cross-validation process. This cross-validation helps ensure that the machine does not generalize information or perform less efficiently against unseen data. In more simplified terms, a machine can learn how to drown out what it has categorized as “background noise,” which may result in an inability to generalize new data due to its bias towards whatever data it has prioritized as relevant. Examples of supervised machine learning techniques include but are not limited to random forest, linear regression, and logistic regression.
Unsupervised Machine Learning Techniques
Unsupervised machine learning is beneficial for identifying fraudulent transactions. If you have ever received a text confirmation from your bank account asking you to confirm a suspicious purchase, chances are unsupervised learning has played a role in it. This type of machine learning is heavily dependent on algorithms focusing on the analysis and clustering of datasets. Unsupervised machine learning helps find similarities, differences, and patterns within data grouping without requiring human intervention. Two examples of unsupervised machine learning algorithms are K-means clustering and probabilistic clustering methods.
Real-world Machine Learning Applications
Most people encounter the fruits of machine learning techniques daily. Autocorrect on your phone’s keyboard, YouTube’s recommended video queue, and Facebook’s photo tag facial recognition are several examples of machine learning applications. Business models can also utilize machine learning through customer service management methods like chatbots. It is also an integral part of business intelligence analytics. Visit TIBCO’s website for additional information about machine learning techniques and how you may be able to implement them into your organization’s business processes. TIBCO is the industry leader in data science software, so you can utilize their resources as a trustworthy source for all things data science-related.
