Machine Learning is a learning technique for Machine to Learn. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning is used by most of the companies now.
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.
Artificial Intelligence (AI) is everywhere. A possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is Machine Learning (ML).
There are several learning methods or learning technique for Machine to learn. They learn from Data and Experiences.
Machine Learning Methods
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabelled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabelled data for training – typically a small amount of labeled data and a large amount of unlabelled data. The systems that use this method are able to considerably improve learning accuracy.
Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
Future of Machine Learning
Machine Learning can be a competitive advantage to any company be it a top MNC or a start-up 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.
Machine Learning Applications
Machine Learning Application are everywhere. Each and every tech giant are using Machine Learning for better applications.
1. 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.
2. 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.
3. 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 focusses 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 the past and accordingly suggests changes to the sales emails.
4. Machine Learning in Healthcare
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
Computer vision is the most significant contributors to the field of Machine Learning. which uses deep learning. It’s an active healthcare application for ML Microsoft’s Inner Eye initiative that started in 2010 and is currently working on an image diagnostic tool.
And many more.
So What’s Next-
Your next step would be to learn Artificial Neural Network and Deep Learning and to build a solid strength in Statistics.
So, for that, you should start with Perceptron and it’s learning algorithm. For that, I’ve already discussed further on this topic here.
Machine Learning Resources