Machine Learning algorithms enable software applications to predict more accurately outcomes. Most of the organizations use top machine learning platforms to build the models that can receive data from the various sources.
While the term connotes the high-tech future envisioned by sci-fi writers, the products, services and capabilities the top machine learning platforms facilitate are very common. For example, recommendation engines based on machine learning personalize online ad delivery, often in near real time. It's no coincidence that a shoe search on Amazon brings up shoe ads on other websites.
As with predictive analytics and data mining, machine learning searches through data to look for particular patterns and trends and then adjusts the actions accordingly. There are different types of machine learning algorithms that vary from fairly simple to highly complex. Following are the examples:
Decision trees, which are models that use observations about certain actions and identify an optimal path to arrive at a desired outcome.
Clustering brings a specified number of data points into a specific number of groups based on their similar characteristics.
Reinforcement learning, an area of deep learning that involves models iterating over many attempts to complete a process.
Supervised Machine Learning vs. Unsupervised Machine Learning:
Machine Learning is divided into two categories like supervised and unsupervised Machine Learning.
Supervised Machine Learning.
Unsupervised Machine Learning.
Supervised machine learning algorithms require a data scientist or data analytics with machine learning related skills that provide both the input and the desired output. They can also offer feedback about prediction accuracy during algorithm training.
In addition, data scientists determine which variables the model should analyze and use to develop predictions. Once trained, an algorithm applies what it has learned to new data.
Unsupervised machine algorithms don't require training with the desired outcome data. These deep learning algorithms review data and arrive at conclusions. Neural networks identify correlations between multiple variables in large amounts of data, using what they learn to process incoming data in the future. They're better suited to more complex processing tasks including image recognition, speech to text and natural language generation than supervised learning systems.
Machine learning and data science
Machine learning and data science are distinct, and both platforms differ from business/data analytics platforms. Specifically, data science platforms are software hubs in which data science work, such as integration, coding, model building and data exploration, takes place in an organization.
Data analytics platforms perform data analytics operations. Through these platforms, organizations use tools to conduct analytics in order to gain business insights from data. These platforms support data-intensive applications, as well as clusters of hardware.
Examples of Machine Learning are recommendiation engines, fraud detection, spam filtering and networking security, self-driving cars, virtual assistant and chatbots and predictive maintenance.