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.
2. 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.
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3. 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.
4. Machine Learning Applications
a. Machine Learning in Education
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. 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 allows a more relevant personalization. Thus, companies can interact and engage with the customer. Sophisticated segmentation focus on the appropriate customer at the right time. Also, with the right message. Companies have information which 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
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.
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5. 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.
6. 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.
7. Future of Machine Learning
Machine Learning can be a competitive advantage to any company be it a top MNC or a startup as things that are currently being done manually will be done tomorrow by machines. Machine Learning revolution will stay with us for long and so will be the future of Machine Learning.
As a result, we have studied the future of Machine Learning. Also, study algorithms of machine learning. Along with we have studied its application which will help you to deal with real life. Furthermore, if you feel any query, feel free to ask in a comment section.
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