The human brain has got tremendous potential. On an everyday basis, it performs hundreds and thousands of tasks- such as recognizing faces, learning from experiences, memorizing facts, identifying objects, understanding words & languages, recognize intent & sentiments, and more. There are billions of neurons in the brain that network together to accomplish these tasks.
Today, human intelligence is emulated by machines, using artificial intelligence as the base technology. Machines are made to do everything that the human brain can and in this process, various technologies such as Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, etc. have been playing an important role.
However, the fundamental technology that’s supporting the rest of the AI technologies in achieving their goals is Machine Learning (ML).
Machine Learning is an application of AI that enables the systems to automatically learn and improve from experience, without being explicitly programmed for it. But, how does ML technology makes this possible? What are the key components of machine learning?
In the latter segment of this blog, we are going to list 5 crucial components that allow developers to build a self-learning solution. Before we move on to that, you can have a look at one of our recent articles that will help you to understand artificial intelligence, machine learning, and deep learning in detail. Most of the time, these technologies are interchangeably used. While they are interrelated, they serve a different purpose.
Machines need a lot of data to function, to learn from, and ultimately make decisions based on it. This data can be any unprocessed fact, value, sound, image, text which can be interpreted and analyzed. A data set is a consolidated data of a similar genre that is captured in different environments. For example, a dataset of currency notes will have images of notes captured in different orientations, light, mobile cameras, and background so as to achieve maximum accuracy in notes classification and identification.
Once a dataset is ready, it is used for training, validating, and testing the ML model. Bigger the set of data, better are the learning opportunities for the model, higher are the chances of achieving accuracy in results.
When building a data set, make sure that it has 5V characteristics:
Volume: Scalability of data matters. Bigger the data set, better it is for the ML model. Large data set makes it easy for the model to make the most optimal decisions.
Variety: The data set can have different forms of data such as images and videos. Variety in data has significance in ensuring accuracy in results.
Velocity: The speed at which the data is accumulated in the data set matters.
Value: The data set should have meaningful information on it. Maintaining a big data set with valuable information is necessary.
Veracity: Accuracy in data is important while maintaining a data set. Correctness in data means precision in the output received.
Simply consider an algorithm as a mathematical or logical program that turns a data set into a model. There are different types of algorithms that can be chosen, depending on the type of problem that the model is trying to solve, resources available, and the nature of data.
Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.
In Machine learning, a model is a computational representation of real-world processes. An ML model is trained to recognize certain types of patterns by training it over a set of data using relevant algorithms. Once a model is trained, it can be used to make predictions.
For example, if there is an application that categorizes cars on the basis of their structure, then a model is trained against a data set wherein the images are tagged according to various features. As the model keep recognizing the cars, the accuracy level will keep on increasing with time.
Datasets can have multiple features. If the features in the dataset are similar or vary to a large extent, then the observations stored in the dataset are likely to make an ML model suffer from overfitting.
Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
To overcome this problem, it is necessary to regularize the number of features in data sets by using feature extraction techniques. Feature extraction aims at reducing the number of features in a dataset by creating new features from the existing ones.
To understand how feature extraction works, check out this article. It explains how the transfer learning technique utilizes a feature extraction approach to accelerate the ML model development process.
Training includes approaches that allow ML models to identify patterns, and make decisions. There are different ways to achieve this including supervised learning, unsupervised learning, reinforcement learning, etc. Check out more about them in the article below.
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Machine Learning (ML) models are the baseline of various AI projects. If you’re planning to build an ML model from scratch, then get started with it by setting up a consultation session with our AI experts.