machine learning | types of machine learning | future of machine learning

Machine Learning

As we all know that we are living in a world of machines, and like humans have evolved and learned so much from their past millions of years’ experience, evolving today also and will continue the process further in future also. Similarly, machines and robots are also evolving, though it had just begun. We can also consider it in a way that today we are living in the primitive age of machines while the future of machines is limitless and beyond imagination.

Now, lets discuss what is machine? Machine is something which is created by humans which takes instructions from humans and performs task which reduces the human effort and makes work easier. But what if machine starts learning from its experience, work like humans, feel like humans, and do things more accurately than humans, without being specifically instructed or programmed. This is where the term machine learning comes into picture.

What is Machine Learning?

“Machine Learning” is basically a field of study which allows the machine to learn from its own experience and examples and that too without being programmed or instructed. Machine learning allows the user to feed the data into the computer algorithm and then, the computer analyze the data and provides data driven recommendations or the outputs on the basis of the data which was fed into the computer.

Machine Learning is a subset of AI i.e., “artificial intelligence”, it basically allows the machine to think on its own, and then there is a term “deep learning”, which is a subset of machine learning and artificial intelligence. Deep learning is based on multi-neural networks with representation learning. It imitates the way humans gain certain type of knowledge. In deep learning, there are various techniques, such as ANN (Artificial Neural Network), CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network).

Let’s discuss some of the real-life examples of machine learning. Some of the examples of machine learning are as follows:

·       ·       In health and care industry machine learning can play a vital role in diagnosing of diseases, to assist in formulating a diagnosis or recommends a treatment options, to analyze body fluids.

·       Machine learning can classify available data into groups, which are the defined by rules set by analyst, that ultimately allows analyst to predict whether the transaction is fraud or legitimate, improves prediction system to calculate the possibility of fault.

·       Machine learning also helps in extracting structured information from unstructured data.

·       Machine learning plays a vital role in fortifying surveillance techniques by tracking down terrorists and criminals, making the world a safer place.

·       Machine learning is also used in maps, in which ML algorithms helps to calculate the quickest route having less or no traffic, arrival time, pick-up location and the optimal route to a destination. Machine learning techniques have incorporated a deep learning model to explore transportation traffic, intricate roadway interactions and environmental element.

·       Machine learning in agriculture helps in precise and efficient farming with less man-power for high quality production.

·       Siri, Alexa and Google assistants are some of the smart assistants we use in our everyday life to carryout activities like reminders, alarms, checking weather, etc.

·       Machine learning also helps governments and authorities to manage huge amount of data and analyze real time data.

What are the four basics of Machine Learning?

Machine learning techniques are divided mainly into the following four categories i.e., Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Machine Learning, Semi-supervised Machine Learning.

Supervised Machine Learning

Ø Supervised Machine Learning – Supervised learning as the name indicates the presence of a supervisor as teacher. Basically, supervised learning is a learning in which we teach or train the machine using data which is labelled that means some data is already tagged correct answer. After that machine is provided with a new set of examples (data) so that supervised learning algorithm analyses the training data (set of training examples) and produces a correct outcome from labelled data.

Ø Unsupervised Machine Learning – Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. Unsupervised learning algorithms allows you to perform more complex processing task as compared to supervising learning. Unsupervised can be more predictable compared with other natural learning methods.

Ø Reinforcement Machine Learning – Reinforcement learning can be understood using the concept of agents, environment, states, actions and rewards. It is basically a part of machine learning which deals with how to perform task through repeated trial and even interactions with dynamic environment. Agent is awarded or penalized with a point for a correct or a wrong answer and on the basis of the positive rewarded points gained the model trains itself.

Ø Semi-Supervised Machine Learning – Semi-supervised learning is a combination of both supervised learning and unsupervised learning methods as semi-supervised learning uses unlabeled data as well as labeled data. Semi-supervised machine learning basically pick-up the large unlabeled dataset, label a small portion of the dataset, put the unlabeled dataset into cluster using unsupervised machine learning algorithm, build your model to use the labeled data to label and classify the rest of the unlabeled data.

Reinforcement Machine Learning

Conclusion: 

Machine learning based AI powered applications are all around us, on our GPS, streaming platforms, virtual assistants, evolving everyday and will do a lot more in future. Machine learning is a large market and the largest segment of AI market. As of 2021, Tencent was the largest owner of Machine Learning and Artificial Intelligence patent families worldwide with 9,614 families owned. In 2020, the company claimed the leading position from Microsoft now ranked sixth with 5,821 active families owned.  The experts says that the market is expected to grow from around 22.6 billion U.S. dollars to nearly 126 billion U.S. dollars by 2025.