Future of Machine Learning – Why Learn Machine Learning

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In this blog, we will discuss the future of Machine Learning to understand why you should learn Machine Learning. Also, will learn different Machine learning algorithms and advantages and limitations of Machine learning.

Along with this, we will also study real-life Machine Learning Future applications to understand companies using machine learning.

Introduction to Machine Learning

Basically, it’s an application of artificial intelligence. Also, it allows software applications to become accurate in predicting outcomes. Moreover, machine learning focuses on the development of computer programs.

The primary aim is to allow the computers learn automatically without human intervention. Google says” Machine Learning is the future”, so future of machine learning is going to be very bright.

As humans become more addicted to machines, we’re witness to a new revolution that’s taking over the world and that is going to be the future of Machine Learning.

Machine Learning Algorithm

Generally, there are 3 types of learning algorithm:

a. Supervised Machine Learning Algorithms

To make predictions, we use this machine learning algorithm. Further, this algorithm searches for patterns within the value labels that was assigned to data points.

b. Unsupervised Machine Learning Algorithms

No labels are associated with data points. Also, these machine learning algorithms organize the data into a group of clusters. Moreover, it needs to describe its structure. Also, to make complex data look simple and organized for analysis.

c. Reinforcement Machine Learning Algorithms

We use these algorithms to choose an action. Also, we can see that it is based on each data point. Moreover, after some time the algorithm changes its strategy to learn better. Also, achieve the best reward.

Machine Learning Applications

Machine Learning Applications

a. Machine Learning in Education

Advances in AI are enabling teachers to realize a far better understanding of how their students are progressing with learning.

AI will make big and positive changes in education helping students to enjoy the training process and have a far better understanding with their teachers. Students will not feel apprehensive towards their teachers and be frightened of being judged.

Teachers can use machine learning to check how much of lessons students are able to consume, how they are coping with the lessons taught and whether they are finding it too much to consume. Of course, this allows the teachers to help their students grasp the lessons.

Also, prevent the at-risk students from falling behind or even worst, dropping out.

b. Machine learning in Search Engine

Search engines rely on machine learning to improve their services is no secret today. Implementing these Google has introduced some amazing services. Such as voice recognition, image search and many more.

Google services like its image search and translation tools use sophisticated machine learning which permit computers to ascertain , listen and speak in much an equivalent way as human do.

Machine learning is that the term for the present cutting-edge applications in AI. How they come up with more interesting features is what time will tell us.

c. Machine Learning in Digital Marketing

This is where machine learning can help significantly.

Machine Learning is being implemented in digital marketing departments round the globe It allows a more relevant personalization. Thus, companies can interact and engage with the customer.

As consumer expectations grow for more personalized, relevant, and assistive experiences, machine learning is becoming a useful tool to assist meet those demands.

Sophisticated segmentation focus on the appropriate customer at the right time. Also, with the right message.

Companies have information that can be leveraged to learn their behavior. Nova uses machine learning to write sales emails that are personalized one. It knows which emails performed better in past and accordingly suggests changes to the sales emails.

d. Machine Learning in Health Care

Machine learning, simply put, may be a sort of AI when computers are programmed to find out information without human intervention.

The foremost common healthcare use cases for machine learning are automating medical billing, clinical decision support and therefore the development of clinical care guidelines.

More importantly, scientists and researchers are using machine learning (ML) to churn out variety of smart solutions which will ultimately help in diagnosing and treating an illness.

Patients are set to profit the foremost because the technology can improve their outcome by analyzing the simplest sorts of treatment for them. This application seems to remain a hot topic for the last three years.

Several promising start-ups of this industry as they are gearing up their effort with a focus on healthcare. These include Nervanasys (acquired by Intel), Ayasdi, Sentient, Digital Reasoning System among others.

Computer vision is most significant contributors in the field of machine learning which uses deep learning. It’s an active healthcare application for ML Microsoft’s InnerEye initiative. That started in 2010, is currently working on image diagnostic tool.

Advantages of Machine learning

a. Supplementing data mining

Data mining is the process of examining a database. Also, several databases to process or analyze data and generate information. Data mining means to discover properties of datasets. While machine learning is about learning from and making predictions on the data.

b. Automation of tasks

It involves the development of autonomous computers, software programs. Autonomous driving technologies, face recognition are other examples of automated tasks.

Limitations of Machine Learning

a. Time constraint in learning

It is impossible to make immediate accurate predictions. Also, remember one thing that it learns through historical data. Although, it’s noted that the bigger the data and the longer it is exposed to these data, the better it will perform.

b. Problems with verification

Another limitation is the lack of verification. It’s difficult to prove that the predictions made by a machine learning system are suitable for all scenarios.