Recent trends have seen an increase in the interest of businesses in machine learning and data science. The rising use of mobile phones, increased internet availability to consumers and the rise of Web 2.0 have all resulted in more data being generated by users, while in the past the main sources of data have been businesses and organizations. Increased availability of cheap computing power has made it viable to deploy data science solutions in production, something that has been prohibitively expensive in the past. The rise of IoT has resulted in more data being generated by machines as compared to people.
The availability of massive amounts of data presents both opportunities and challenges for businesses. There has never been more data available for businesses to leverage in order to determine patterns and trends relevant to businesses, for make better forecasts and predictions and for data-driven decision making. However, the size and frequency of new data means that traditional approaches used in analyzing data such as classical statistics cannot scale well in handling the available and new data been generated daily. Data science in general and machine learning, combined with recent computing trends like cheap computing times, provide businesses with a way to leverage the huge amount of information available to businesses.
Types of machine learning
Machine learning is a subset of data science that uses provided data in order to come up with models that can be used in order to make predictions and discover patterns in previously unseen data. In order to come up with such models, four types of machine learning approaches are used. They are supervised learning, semi-supervised learning, unsupervised learning and reinforcement learning. Depending on the task at hand, certain types of algorithms are better suited for implementing a machine learning solution compared to others.
In supervised learning, the machine learning algorithm is provided with data containing both the independent variables (features) and the dependent variable (target). The data is known as the training data. The algorithm then generates a generalized model that can be applied to even data it has never seen before in order to carry out classification and prediction. The model is tested with data by the data scientist with the features and evaluated on how it performs on the previously unseen data.
An example of supervised machine learning will be providing the model with training data consisting of features like people’s salary, loans applied, marital status and age as well as whether the person defaulted on a loan or not. Once the model has been trained and evaluated, it can then be used to predict whether someone whose data it has never seen before will default on a loan or not. Some of the machine learning algorithms used for supervised learning include logistic regression, K-nearest neighbors, decision trees and support vector machine for classification and linear regression, Lasso regression and gradient boosting for regression.
In unsupervised learning, the data is provided to the machine learning algorithm without any labels or target values. Instead, the idea behind using unsupervised learning is to discover patterns and trends in the data without being explicitly guided, mainly through clustering and hierarchies. Some of the algorithms used include C-Means, K-Means, Hidden Markov Chains and K-Medoids for clustering and Principal Component Analysis and Linear Discriminant Analysis for dimensionality reduction, which is a way of determining the most important features to reduce the data and computing power required by the model developed.
In semi-supervised learning, the machine learning algorithm is provided both with labeled data for training and unlabeled data. This allows the data scientist to label a subset of the available data and the provided the unlabeled data for the algorithm to leverage. The motivation for providing the unlabeled data is to provide the algorithm with more data to work with, leading to development of a better and more robust model. Some of the algorithms in this category include help-training algorithms, graph-based algorithms, self-training algorithms, generative model algorithms and transductive SVM.
In reinforcement learning, the model learns by interacting with its environment. When interacting with the environment, various actions normally result in change in states. The actions of the agent in the environment result in either a reward or punishment, with the goal of the agent being to maximize the long-term rewards. The model is able to learn from previous experiences in order to better make decisions. Some of the algorithms used for unsupervised learning are Markov decision process, dynamic programming algorithms, value optimization algorithms and policy optimization algorithms.
Applications of the different types of machine learning to business scenarios
Supervised learning is primarily used for assigning a label to some observations e.g. spam/ not spam, default/ will not default and to predict a numerical value/ range e.g. the price of a stock at close of trading. These are known as classification and regression respectively. Depending on the data available, businesses can either carry out classification or prediction as per the requirements of the data science project.
Businesses will typically employ various supervised machine learning algorithms in order to make better data-driven decisions e.g. whether or not to approve a loan and how to price real estate and to make predictions and forecasts e.g. the prices of real estate in a certain region in 5 years’ time. Some real-world business application of supervised learning are in email spam detection, fraud detection and image classification in classification and score prediction and risk assessment for regression.
Unsupervised learning is mainly used to determine patterns and trends in data that a data scientist might miss or be unable to detect. Some areas in businesses where unsupervised learning is being used in the real world by businesses include high dimensional data visualization, text mining, image recognition and face recognition as applied to dimensionality reduction and targeted marketing, customer segmentation and city planning when applied to clustering and market basket analysis and shelf-space allocation optimization when using association.
Semi-supervised learning combines elements of supervised and unsupervised learning. Current business applications include using GPS data for route planning and text classification. Reinforcement learning is well-suited to problems involving a decision-making agent (the machine) and its interaction with an external environment. Some of the areas businesses are applying reinforcement learning is in self-driving cars and optimized marketing.
Machine learning and data science have revolutionized the way businesses leverage the data available to them to better meet their objectives. Whether it is making forecasts and predictions or using data to drive decision-making, businesses of all sizes and in all industries can use machine learning to improve their bottom line and to ensure they are making the best decisions in order to meet their objectives.
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