Machine Learning

The overall size of global markets for machine learning and artificial intelligence-based solutions are highly limited but are on demand. With time machine-learning applications are becoming more accessible and affordable at the enterprise level.

Machine learning is a category of algorithm that allows software applications to become more correct in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

Types of machine learning algorithms
Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the equally simple to the highly complex. Here are a few of the most commonly used models:

The machine learning algorithm involves identifying an association generally between two variables and using that correlation to make predictions about future data points.

  • Decision trees :
    These models use explanations about certain actions and identify an optimal path for arriving at the desired outcome.
  • K-means clustering :
    This model groups a specified number of data points into a specific number of groupings based on like characteristics.
  • Neural networks :
    These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.
  • Reinforcement learning :
    This area of deep learning involves models repeating over many attempts to complete a process

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