Define machine learning: 5 machine learning types to know

Machine learning (ML) can be used in computer vision, large language models (LLMs), speech recognition, self-driving cars, and many more use cases to make decisions in healthcare, human resources, finance, and other areas

ML validation and training datasets are generally aggregated by humans, who are biased and error-prone. Even if an ML model isn’t biased or erroneous, using it incorrectly can cause harm

ML models optimize performance utilizing algorithms and statistical models that deploy jobs based on data patterns and inferences

Machine learning algorithms recommend products based on purchasing history on retail websites. IBM, Amazon, Google, Meta, and Netflix use ANNs to make tailored suggestions on their e-commerce platforms

Supervised machine learning trains the model on a labeled dataset with the target or outcome variable known

Cluster analysis is the most frequent unsupervised learning method, which groups data points by value similarity for customer segmentation and anomaly detection

SSL algorithms, also known as predictive or pretext learning algorithms automatically classify and solve unsupervised problems by learning one portion of the input from another

Dynamic programming dubbed reinforcement learning from human feedback (RLHF) trains algorithms using reward and punishment