What is Machine Learning or Cognitive Computing?

 Machine Learning, also known as cognitive computing, is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data without being explicitly programmed. Machine learning algorithms learn from experience and improve their performance over time through exposure to new data.

Machine Learning Algorithms Can be Broadly Categorized into Three Types:

1. Supervised Learning: In this type of machine learning, the algorithm is trained on labelled data, where the input data points are associated with known output labels. The algorithm learns to make predictions or decisions based on the labelled examples and generalizes the learned patterns to new, unseen data. Common supervised learning tasks include classification (predicting discrete labels) and regression (predicting continuous values).

2. Unsupervised Learning: In unsupervised learning, the algorithm is trained on unlabeled data, where the input data points do not have associated output labels. The algorithm learns to identify patterns, structures, or relationships in the data without any explicit guidance. Common unsupervised learning tasks include clustering (grouping similar data points) and dimensionality reduction (reducing the number of variables or features in the data).

3. Reinforcement Learning: Reinforcement learning involves training an algorithm to make decisions or take actions in an environment to maximize a cumulative reward signal. The algorithm learns to make decisions based on trial-and-error interactions with the environment and receives feedback in the form of rewards or penalties based on its actions. Reinforcement learning is commonly used in robotics, autonomous vehicles, game-playing, and recommendation systems.

Machine learning is used in a wide range of applications, including natural language processing, image recognition, speech recognition, recommendation systems, fraud detection, predictive analytics, autonomous vehicles, personalized medicine, and many more. It has the potential to revolutionize industries and enable organizations to leverage data for better decision-making, process optimization, and innovation.