What is Machine Learning

July 12, 2021 | 320 Views

Machine Learning

Machine Learning is defined as a technology that uses historical data or past experience to train machines to do various tasks such as predictions, suggestions, estimations, and so on. Machine Learning teaches machines to behave like humans by using past experience and projected data to train them.

Techniques in Machine Learning

Machine Learning techniques are divided mainly into the following 4 categories:

1. Supervised Learning

When a machine has sample data, that is, input and output data with correct labels, supervised learning can be used. Using some labels and tags, correct labels are utilised to check the model's validity. The supervised learning technique uses past experience and labelled samples to predict future outcomes. It first analyses the known training dataset before introducing an inferred function that predicts output values. Furthermore, it detects faults and corrects them using algorithms during the learning process.

Example: Assume we have a series of photos labelled as "dog". With these dog photos, a machine learning system is trained to readily detect whether an image is of a dog or not.

2. Unsupervised Learning

Unsupervised learning involves training a system with only a few input samples or labels, with no knowledge of the result. Because the training data is not classified or labelled, a machine may not always produce proper results when compared to supervised learning.

Although unsupervised learning is less frequent in business, it aids in data exploration and can draw inferences from datasets to describe hidden structures in unlabeled data.

Assume a computer has been taught with a set of documents with multiple categories (Type A, B, and C), and we need to classify them into relevant groups. Because the machine is just given input samples or no output, it can group these datasets into type A, type B, and type C categories, but it is unimportant whether the organisation is correct or not.

3. Reinforcement Learning

Reinforcement Learning is a machine learning technique that is based on feedback. In this sort of learning, agents (computer programmes) must explore their environment, take actions, and receive rewards as feedback based on their behaviours. They receive a positive reward for each good action and a negative reward for each bad one. A Reinforcement learning agent's purpose is to maximise positive rewards. Because there is no labelled data, the agent can only learn by experience.

4. Semi-supervised Learning

Semi-supervised learning is a technique that bridges the gap between supervised and unsupervised learning. It operates on datasets with minimal labels as well as unlabeled data. It does, however, typically contain unlabeled data. As a result, it lowers the cost of the machine learning model because labels are expensive, but for corporate purposes, it may have few labels. It also improves the machine learning model's accuracy and performance.

Sem-supervised learning assists data scientists in overcoming the limitations of both supervised and unsupervised learning. Semi-supervised learning has several applications, including speech analysis, online content categorization, protein sequence classification, text document classifiers, and so on.

Applications of Machine Learning

Machine Learning is widely being used in approximately every sector, including healthcare, marketing, finance, infrastructure, automation, etc. There are some important real-world examples of machine learning, which are as follows:

Healthcare and Medical Diagnosis:

Machine Learning is employed in the healthcare industry to assist in the generation of neural networks. These self-learning neural networks assist doctors in providing quality care by assessing external data about a patient's status, such as X-rays, CT scans, and numerous tests and screenings. Aside from treatment, machine learning is useful in areas such as computerised billing, clinical decision assistance, and the establishment of clinical care recommendations, among others.

Marketing:

Machine learning assists marketers in developing hypotheses, testing, evaluating, and analysing datasets. It enables us to generate quick forecasts based on the concept of big data. It is particularly useful for stock marketing because the majority of trading is done by bots and is based on calculations from machine learning algorithms. Convolutional Neural Network, Recurrent Neural Network, Long-short Term Memory, and other Deep Learning Neural Networks aid in the development of trading models.

Self-driving cars:

This is one of the most exciting machine learning applications in today's globe. It is critical in the development of self-driving cars. Various automobile firms, such as Tesla and Tata, are constantly attempting to produce self-driving automobiles. It is also made possible by the supervised learning machine learning method, in which a machine is trained to recognise people and objects while driving.

Speech Recognition:

One of the most popular uses of machine learning is speech recognition. Almost every smartphone application now includes a voice search feature. This "Search By Voice" feature is also related to voice recognition. Speech to text" or "Computer speech recognition" is the process through which voice commands are transformed into text.

Some well-known speech recognition programmes include Google Assistant, SIRI, Alexa, Cortana, and others.

Traffic Prediction:

Using Google Maps, machine learning can also help us find the shortest route to our location. It also assists us in estimating traffic conditions, whether they are clear or crowded, thanks to the Google Maps app and sensor's real-time location.

Image Recognition:

Image identification is another major use of machine learning for identifying objects, people, and places, among other things. Face detection and auto friend tagging suggestion is the most well-known picture recognition application utilised by Facebook, Instagram, and others. When we post images of our Facebook pals, image recognition technology offers their names to us.

Product Recommendations:

Machine Learning is commonly utilised in business industries for product marketing. Almost all large and small businesses, like Amazon, Alibaba, Walmart, and Netflix, employ machine learning algorithms to recommend products to their customers. Whenever we look for a product on their websites, we are bombarded with adverts for similar things. This is also possible using Machine Learning algorithms, which learn about users' preferences and recommend things to them based on historical data.

Automatic Translation:

Automatic language translation is another important application of machine learning, which is based on sequence algorithms and involves translating text from one language into other desirable languages. This feature, known as Neural Machine Learning, is provided by Google GNMT (Google Neural Machine Translation). You can also use Google Lens to translate selected text on photos as well as entire documents.

Virtual Assistant:

A virtual personal assistant is another prominent machine learning application. It first records the speech and transfers it to a cloud-based server, where it is decoded using machine learning techniques. All major corporations, such as Amazon and Google, use these features to play music, call someone, open an app, and search for data on the internet, among other things.

Email Spam and Malware Filtering:

Machine Learning also assists us in categorising emails that arrive in our inbox, such as important, regular, and spam. ML algorithms such as Multi-Layer Perceptron, Decision Tree, and Nave Bayes classifier make this possible.