Buyer's Handbook: Best machine learning platforms comparison and buyer's guide Article 2 of 3

What do businesses do with the top machine learning platforms?

Take a deep dive into machine learning, including decision trees, clustering, reinforced learning, neural networks, as well as supervised and unsupervised machine learning.

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.

Machine learning is a category of algorithm that enables software applications to more accurately predict outcomes without explicit programming. Organizations use the top machine learning platforms to build models that can receive data from various sources and apply statistical analysis in order to predict an output, while also updating the output as new data becomes available.

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. Some examples include:

  • decision trees, which are models that use observations about certain actions and identify an optimal path to arrive at a desired outcome;
  • clustering, which brings together a specified number of data points into a specific number of groupings based on similar characteristics; and
  • 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 styles break down into two distinct categories: supervised and unsupervised.

Supervised machine learning algorithms require a data scientist or data analyst with machine learning-related skills who can provide both the input and the desired output. They can also offer feedback about prediction accuracy during algorithm training.

It's no coincidence that a shoe search on Amazon brings up shoe ads on other websites.

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 scientists and analysts must identify relevant data before training a machine learning algorithm.
Supervised machine learning occurs under the watchful eye of a data scientist or other analyst.

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

Machine learning supports a growing number of use cases that are impacting businesses in a big way. Here are a few examples:

Recommendation engines

Recommendation -- or recommender -- engines or systems are data filtering platforms that aim to predict user preference or a user rating for a particular item. Websites offering products and services such as books, movies, music, news, search queries and other things use these systems, as do financial services companies, insurance companies, restaurants and online dating businesses.

These engines often produce recommendations based on collaborative filtering, a model built on a user's past behavior or on similar decisions made by other users. These engines can also use content-based filtering, which identifies an item's discrete characteristics to recommend additional items with similar traits.

Fraud detection

Traditional methods of analyzing data to detect fraud are time-consuming. Machine learning can play a key role by clustering and classifying data to find patterns that might indicate fraudulent behavior. Security teams can use this technology to automatically identify characteristics of fraud. Neural networks, in particular, can learn suspicious patterns from data samples.

Spam filtering and network security

The top machine learning platforms can help filter out junk email from legitimate communication. It builds spam classification models that distinguish between legitimate emails and spam. The ultimate goal is to create efficient models that provide a high level of accuracy with a low false positive rate.

Because many ransomware and phishing attacks come wrapped as spam, this also benefits network security. In addition, machine learning can detect the presence of network intruders and malicious insiders looking to launch a data breach. Algorithms help organizations detect malicious activity quickly and stop attacks before they can do damage.

Self-driving cars

The top machine learning platforms can enable self-driving cars. Deep learning neural networks can identify objects in the road or in the general environment and determine the best action to safely guide the vehicle.

Machine learning evaluates driving scenarios using data gathered by various external and internal sensors, such as radar and cameras.

Virtual assistants/chatbots

Virtual assistants and chatbots, two applications that understand natural language and voice commands, can complete tasks traditionally performed by personal assistants or secretaries, including taking dictation, looking up phone numbers and reminding users about appointments.

The technologies that power virtual assistants and AI chatbots need massive amounts of data, and machine learning is a key component.

Predictive maintenance

For companies that rely on complex machinery or devices, being able to predict and address malfunctions or declining performance before they occur can prevent serious and costly problems. Predictive maintenance powered by the top machine learning platforms can enable them to do this, potentially preventing downtime in factories and other facilities that rely on the equipment.

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