Machine learning can be viewed as a self-teaching computer. The machine learning definition can be understood as a system that can learn by itself without explicit training. This is the answer to your question: what is ML? Machine learning is an integral component of Artificial intelligence. In simple words, it is the field of data science. Machine learning means that the computer algorithms improve themselves by using the previous data. Machine learning algorithms build themselves based on sample data, otherwise known as training data. Machine learning systems use statistical methods to train the algorithms to make classifications, predictions, reports, analyses, and much more. Since it is data-driven and thrives on it, machine learning is ideal for sectors that receive volumes of data every day. Machine learning in artificial intelligence finds its application in medicine, email filtering, speech recognition, accounting, and finance. It is speedily making a mark in other sectors such as banking and investment as well.
Since you know what ML is, let's see what sets it apart. Many sources still quote that machine learning falls under AI completely. However, a few only consider the intelligence subset of machine learning as a part of AI. Machine learning as a field differs from artificial intelligence in its aim. It no longer aims to achieve artificial intelligence but seeks to solve practical problems. Before we learn how machine learning, deep learning, and neural networks differ, you must note that they are all subfields of artificial intelligence. While deep machine learning falls under the realm of machine learning, neural networks are like the spine of deep learning. What sets deep learning apart from machine learning is its ability to perform complex tasks the way humans perform them. For example, machine learning cannot collect data from images and videos as easily as deep learning. Machine learning systems demand more human intervention, especially for training. Deep machine learning, on the other hand, once set up, requires minimal intervention. Similarly, a machine learning system is less complex as compared to deep machine learning. Even though machine learning is easier to set up than deep learning, the results that you will get from deep machine learning are much better. Another difference between the two is that machine learning algorithms make use of structured data, whereas deep machine learning can make use of structured as well as unstructured or raw data. Lastly, deep machine learning employs neural networks. In contrast, machine learning makes use of traditional algorithms like linear regression. Neural networks are like the human brain that passes information from one node to another that forms layers. The difference between neural networks and deep learning is the number of layers or the depth of the layers. Neural networks that comprise more than three layers are known as deep neural networks or deep learning algorithms. In contrast, neural networks that have 2-3 layers are basic neural networks.
Concerning the nature of the signal and the feedback available to the learning system, there are three approaches to machine learning - Supervised, Unsupervised, and Reinforcement. Supervised machine algorithms are the mathematical models that contain both the input and the output for the training data, and each of these training data has a supervisory signal. The supervisory signal refers to one or more inputs and the desired output for the training data. Here, a data scientist feeds the specified input and output algorithm to the system. This approach is ideal for binary classification, multi-class classification, regression modeling, or ensembling. In contrast to the supervised learning approach, unsupervised learning works on data that only contains the specified inputs and finds the data's structure itself. It identifies the commonality (similarities or differences) of the data and then reacts to it. As such, an unsupervised machine learning algorithm is for analyzing and grouping unlabeled sets of data. The ability of this approach to discover the commonalities without human intervention makes it an ideal choice for exploratory data analysis, summarizing data sets, cross-selling strategies, image and pattern recognition. Reinforcement learning teaches the machine how to complete a multi-step process. These processes have clearly defined rules. The role of the data scientist here is to feed the system with cues as it figures out how to complete the task. That is, it makes use of a trial and error method. You reinforce the successful outcomes to develop the best solution for the problem. Semi-supervised learning is just like the middle road between a supervised learning algorithm and an unsupervised learning algorithm. Here, the data scientist feeds the system labeled data and allows it to analyze the data for itself and develop its understanding. In cases where you do not have enough labeled data set to train a supervised learning algorithm, the semi-supervised algorithm can rescue you.
Machine learning works on training data sets. If you feed the system with something which wasn't represented in the data sets, it might predict it accurately. An example of this can be training data based on the needs of the current customers. When a new customer comes into the picture, the system might not accurately predict and analyze their needs. Moreover, humans are prone to be biased, intentionally or otherwise. When the system feeds on man-made data, the system may pick up these biases. For example, the system recognizes all black people as gorillas and as a result, fails to identify real gorillas. The system may also learn to recognize all things as one without considering other factors like spatial relationships. For example, if you feed and train the system using photos of black cats and brown dogs, the system may conclude that all brown things are dogs or that all black things are cats.
Machine learning is a growing field of data science and finds its application in several spheres like banking, bioinformatics, insurance, fraud detection, sentiment analysis, and speech recognition. While its growth is inevitable, the dilemma of whether it will replace human employees altogether is a constant. The integration of machine learning may change the job description of the employees. However, it can never replace them totally. They are brought into action by humans and will always need them for supervision and regulation. The system can act like a human mind but cannot become it.
A real-life example of machine learning is predicting whether a transaction is fraudulent or not depending on the user’s past transaction history. Another example can be speech recognition software devices like Alexa or Google Home.
Machine learning finds its application for several purposes like sorting out spam emails, making personalized recommendations, detecting fraud transactions, Robo-advisory, loan underwriting, etc.
Machine learning in simple terms is the computer’s ability to learn by itself by using previous data to become more accurate without being actually trained.