Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without complicated programming. When ingesting new data, these computers learn, grow, change, and develop by themselves. The concept of machine learning has been around for a while now. However, the ability to automatically and quickly apply mathematical calculations to big data is now gaining a bit of momentum. Machine learning has been used in several places like the self-driving cars, the online recommendation engines – friend recommendations on Facebook, offer suggestions from Amazon, and in cyber fraud detection.
Data analysis has traditionally been characterized by the trial-and-error approach – one that becomes impossible to use when there are significant and diverse data sets in question. The availability of more data is directly proportional to the difficulty of bringing in new predictive models that work accurately. Traditional statistical solutions are more focused on static analysis that is limited to the analysis of samples that are frozen in time. This could result in unreliable and inaccurate conclusions.
Coming as a solution to all this chaos is Machine Learning proposing smart alternatives to analyzing vast volumes of data. It is a leap forward from computer science, statistics, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data.
Common Machine Learning models
Binary Classification: In machine learning, binary classification is a supervised learning algorithm that categorizes new observations into one of two classes.
Examples of Binary Classification Problems
- “Is this email spam or not spam?”
- “Will you recommend this to a friend?”
- “Is this review written by a customer or a robot?
Regression: A technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a method for predictive modelling in machine learning, in which an algorithm is used to predict continuous outcomes.
Examples of this are:
- Forecasting continuous outcomes like house prices, stock prices, or sales.
- Predicting the success of future retail sales or marketing campaigns to ensure resources are used effectively.
- Predicting customer or user trends, such as on streaming services or e-commerce websites.
Multiclass Classification: Multiclass classification is a machine learning classification task that consists of more than two classes, or outputs. For example, using a model to identify animal types in images from an encyclopedia is a multiclass classification example because there are many different animal classifications that each image can be classified as. Multiclass classification also requires that a sample only have one class (ie. a dolphin is only a dolphin; it is not also a gator).
Common Machine Learning Algorithms
Reinforcement Learning: Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. In reinforcement learning, developers devise a method of rewarding desired behaviors and punishing negative behaviors. This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors. This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution.
These long-term goals help prevent the agent from stalling on lesser goals. With time, the agent learns to avoid the negative and seek the positive. This learning method has been adopted in artificial intelligence (AI) as a way of directing unsupervised machine learning through rewards and penalties.
Deep Learning: Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.
“Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).”
Clustering: Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as “A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.” t does it by finding some similar patterns in the unlabelled dataset such as shape, size, color, behavior, etc., and divides them as per the presence and absence of those similar patterns. It is an unsupervised learning method, hence no supervision is provided to the algorithm, and it deals with the unlabeled dataset. After applying this clustering technique, each cluster or group is provided with a cluster-ID. ML system can use this id to simplify the processing of large and complex datasets.
Free Courses to upskill your knowledge in Machine Learning:
- Machine Learning – Stanford University
- IBM Machine Learning – IBM Skills Network
- Machine Learning – University of Washington
- Mathematics for Machine Learning – Imperial College London
- Machine Learning for All – University of London
Machine Learning is a powerful tool that is transforming the way we analyze and process data. Its ability to learn, adapt and develop by itself makes it an essential component of artificial intelligence. Traditional statistical methods are limited to static analysis and small data sets, while Machine Learning can process vast amounts of data in real-time, producing accurate results and analysis.
In this article, we discussed some of the common Machine Learning models and algorithms like Binary Classification, Regression, Multiclass Classification, Reinforcement Learning, Deep Learning, and Clustering. Each model has its specific uses, and choosing the right one depends on the task at hand. Machine Learning is a rapidly growing field with many exciting opportunities Upskilling yourself in this area is definitely worth it. There are several free online courses available that can help you get started on your journey to mastering this field.
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