Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on building applications that can automatically and periodically learn and improve from experience without being explicitly programmed.
This branch of artificial intelligence can enable systems to identify patterns in data, make decisions, and predict future outcomes. Machine learning can help companies determine the products you're most likely to buy and even the online content you're most likely to consume and enjoy.
In effect, machine learning is an attempt to teach computers to think, learn, and act like humans. Thanks to increasing internet speeds, advancements in storage technology, and expanding computational power, machine learning has exponentially advanced and become an integral part of almost every industry.
How does machine learning work?
At its heart, machine learning algorithms analyse and identify patterns from datasets and use this to make better predictions on new data sets.
It's similar to how humans learn and improve. Whenever we make a decision, we consider our past experiences to assess the situation better. A machine learning model does the same by analysing historical data to make predictions or decisions. After all, machine learning is an AI application that enables machines to self-learn from data.
Consider the following sequence.
3 - 9
4 - 16
5 - 25
So if you were given the number 6, which number would you pick so that the pair would match the above sequence?
If you concluded that it’s 36, how did you do it?
You probably analysed the previous data (historical data) and "predicted" the number with the highest probability. A machine learning model is no different. It learns from experience and uses the accumulated information to make better predictions. In essence, machine learning is pure math.
4 types of machine learning methods
Supervised learning is a machine learning approach in which a data scientist acts like a tutor and trains the AI system by feeding basic rules and labeled datasets. The datasets will include labeled input data and expected output results. In this machine learning method, the system is explicitly told what to look for in the input data
Unsupervised learning is a machine learning technique in which the data scientist lets the AI system learn by observing. The training dataset will contain only the input data and no corresponding output data.
Semi-supervised learning is an amalgam of supervised and unsupervised learning. In this machine learning process, the data scientist trains the system just a little bit so that it gets a high-level overview.
Reinforcement learning (RL) is a learning technique that allows an AI system to learn in an interactive environment. A programmer will use a reward-penalty approach to teach the system, enabling it to learn by trial and error and receiving feedback from its own actions.
Uses of machine learning
It's safe to say that machine learning has impacted almost every field that underwent a digital transformation. This branch of artificial intelligence has immense potential when it comes to task automation, and its predictive capabilities are saving lives in the healthcare industry.
Some application examples include:
Image recognition
Machines are getting better at processing images. In fact, machine learning models are better and faster in recognizing and classifying images than humans.
This application of machine learning is called image recognition or computer vision. It's powered by deep learning algorithms and uses images as the input data. You have most likely seen this feat in action when you uploaded a photo on Facebook and the app suggested tagging your friends by recognizing their faces.
Patient diagnosis
It's safe to say that paper medical records are a thing of the past. A good number of hospitals and clinics have now adopted electronic health records (EHRs), making the storage of patient information more secure and efficient.
Since EHRs convert patient information to a digital format, the healthcare industry gets to implement machine learning and eradicate tedious processes. This also means that doctors can analyse patient data in real time and even predict the possibilities of disease outbreaks.
Inventory optimisation
If a specific material is stored in excess, it may not be used before it gets spoiled. On the other hand, if there's a shortage, the supply chain will be affected. The key is to maintain inventory by considering the product demand.
The demand for a product can be predicted based on historical data. For example, ice cream is sold more frequently during the summer season (although not always and everywhere). However, numerous other factors affect the demand, including the day of the week, temperature, upcoming holidays, and more.
Computing such micro and macro factors is virtually impossible for humans. Not surprisingly, processing such massive volumes of data is a specialty of machine learning applications.
Self-driving cars, demand forecasting, speech recognition, recommendation systems, and anomaly detection wouldn't have been possible without machine learning.
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