Best Machine Learning Software

Machine learning algorithms make predictions or decisions based on data. These learning algorithms can be embedded within applications to provide automated, artificial intelligence (AI) features or be used in an AI platform to build brand new applications. In both cases, a connection to a data source is necessary for the algorithm to learn and adapt over time. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others.

These learned algorithms may be developed with supervised learning or unsupervised learning. Supervised learning consists of training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised learning, on the other hand, requires no consistency in the input of machine learning algorithms. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment. For example, autonomous driving cars are an instance of reinforcement machine learning because they react based on their surroundings on the road. If a traffic light is red, the car stops. Machine learning algorithms are used by developers when using an AI platform to build an application or to embed AI within an existing application. End users of intelligent applications may not be aware that an everyday software tool is utilizing a machine learning algorithm to provide some form of automation. Additionally, machine learning solutions for businesses may come in a machine learning as a service model.

To qualify for inclusion in the Machine Learning category, a product must:

  • Offer an algorithm or product that learns and adapts based on data
  • Be the source of intelligent learning capabilities for applications
  • Consume data inputs from a variety of data pools
  • Provide an output that solves a specific issue based on the learned data

Machine Learning Software Grid® Overview

The best Machine Learning Software products are determined by customer satisfaction (based on user reviews) and scale (based on market share, vendor size, and social impact) and placed into four categories on the Grid®:
  • Products in the Leader quadrant are rated highly by G2 Crowd users and have substantial Market Presence scores. Leaders include: BigML and scikit-learn
  • High Performers are highly rated by their users, but have not yet achieved the market share and scale of the Leaders.
  • Contenders have significant Market Presence and resources, but have received below average user Satisfaction ratings or have not yet received a sufficient number of reviews to validate the solution.
  • Niche solutions do not have the Market Presence of the Leaders. They may have been rated positively on customer Satisfaction, but have not yet received enough reviews to validate them. Niche products include: FloydHub
G2 Crowd Grid® for Machine Learning
Leaders
High Performers
Contenders
Niche
Bigml
Scikit learn
Floydhub
Market Presence
Satisfaction
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