Machine learning is the application of artificial intelligence so as to enable machines to learn from a given dataset and make accurate predictions without human interference. software programs then use these predictions to modify the machine’s actions and offer improved results for a given task.A large volume of data, and computing power is key to enabling machine learning. Machine learning models are basically mathematical functions that are capable of repeatedly evolving to interpret the data up to a point where they are able to make accurate predictions for any new data entered.
Methods of Machine Learning
Machine learning fundamentals are classified into three approaches. Supervised machine learning, unsupervised machine learning, and semi-supervised machine learning. The three approaches basically involve feeding structured, unstructured, or mixed data to machines for training.
Supervised machine learning
Supervised machine learning trains the machine by feeding it large amounts of data that is properly labeled. For example, if a machine is to be trained to recognize cats and dogs in pictures, it will fed millions of images of cats and gods where the animals are labeled. Around 80% of this data could be labeled, and 20% could be left for the machine to resolve via learning.
Supervised ML needs large amount of labeled data. Before entering the data for training, it needs to be prepared by removing errors and instances of duplication of data. Big companies generally use crowd-working services such as Amazon Mechanical Truck to prepare and label the data for supervised machine learning models.
Unsupervised machine learning
In unsupervised machine learning, the data is unstructured and unlabeled. It is left to the ML algorithms to spot patterns in the data and classify it. Due to this, there is no certainty about the predictions of unsupervised machine learning algorithms.
Still, it is an important AI tool. It is used to spot irregularities in data such as fraudulent transactions or uncover hidden patterns in data that may not be obvious at the first glance. For example, unsupervised ML could help businesses discover which products are bought together in the market, and accordingly develop their marketing strategy.
However, even if unsupervised machine learning is good at clustering data, it is not considered suitable for use in customer profiling because it classifies data into groups and does not take into account individual data points.
Semi-supervised machine learning
Semi-supervised machine learning gives the machine a little bit of structured data and a larger amount of unstructured data First, a model is trained partially with labeled data. Then this model is used to label the rest of the unstructured data. This process is called pseudo-labeling. Semi-supervised learning can enable the machines to study two datasets and then generate completely new data using the two examples.
Although supervised machine learning is considered as the most reliable and optimal tool among the three approaches, Semi-supervised machine leaning is emerging as a viable option due to the rise of applications such as Generative Adversarial Networks (GANs). As the footprint of semi-supervised machine learning grows, the need for more computing power will be felt more than the need for data as it happens currently with supervised ML.
Advantages of machine learning
Automation
Machine learning enables machines to make decisions on their own, saving considerable time and effort for humans. A good example of machine learning is chat-bots who interact with customers on social media on behalf of the company. They can instantly reply to customers reassuring them that the firm has quick customer support.
Continuous improvement
ML algorithms are capable of evolving with experience. Over time, they can make more accurate predictions faster. As the dataset grows, their performance improves even more.
Spotting the trends
All the three machine learning systems are basically advanced at spotting patterns in data. What’s more ML algorithms spot patterns that may not be visible to humans from a vase amount of data. Thus, various problems of classification can be solved with ease.
Numerous application areas
Today, ML is used in multiple application areas ranging from healthcare to marketing and banking. They are enabling companies to generate insights, cut costs, and predict future outcomes, tremendously improving business efficiency and boosting growth.
The difference between machine learning and AI
Even though they might appear to be similar, AI and machine learning are two different areas. Machine learning uses data to look or patterns and build predictions based on that. Also, machine learning needs data to be fed into the system. AI on the other hand, acquires new knowledge and applies it to build a new skill in uncertain circumstances.
