Deep Insights into Machine Learning

Machine learning (ML) is the subset of artificial intelligence (AI) that focuses on building systems that learn, or improve performance, based on the data they consume, that is, it teaches computers to learn from experience. Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing. An important distinction is that although all machine learning is AI, not all AI is machine learning. These days, machine learning is at work all around us. When we interact with banks, shop online, or use social media, machine learning algorithms come into play to make our experience efficient, smooth, and secure. Machine learning uses two types of techniques: Supervised learning trains a model on known input and output data so that it can predict future outputs, Supervised machine learning algorithms are the most commonly used. With this model, a data scientist acts as a guide and teaches the algorithm what conclusions it should make. Just as a child learns to identify fruits by memorizing them in a picture book, in supervised learning, the algorithm is trained by a dataset that is already labeled and has a predefined output. Examples of supervised machine learning include algorithms such as linear and logistic regression, multiclass classification, and support vector machines. Unsupervised learning finds hidden patterns or intrinsic structures in input data.Unsupervised machine learning uses a more independent approach, in which a computer learns to identify complex processes and patterns without a human providing close, constant guidance. Unsupervised machine learning involves training based on data that does not have labels or a specific, defined output. To continue the childhood teaching analogy, unsupervised machine learning is akin to a child learning to identify fruit by observing colors and patterns, rather than memorizing the names with a teacher’s help. The child would look for similarities between images and separate them into groups, assigning each group its own new label. Examples of unsupervised machine learning algorithms include k-means clustering, principal and independent component analysis, and association rules. Machine learning has blossomed across a wide range of industries, supporting a variety of business goals and use cases including: • Customer lifetime value • Anomaly detection • Dynamic pricing • Predictive maintenance • Image classification • Recommendation engines Hence choosing which approach is best for your needs, be it supervised or unsupervised machine learning algorithm, usually depends on factors related to the structure and volume of your data, and the use case to which you want to apply it. At Hampshire Heights we are able to offer deeper insights to our clients in ways that impact their businesses positively.