In Machine Learning, machines or computers will learn by analyzing examples and experiences. There is no need to write a tough program for the machine. Instead of coding, we have to provide algorithms to machines, so it can perform the tasks according to the given data.
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In this blog “what is machine learning” I will make you to understand about it and start with ML online course. This blog guides you towards the following.
a) Use cases in ML.
b) Reinforcement learning.
c) Unsupervised learning.
d) Supervised learning.
e) Working Principles of ML.
f) How Machines invented?
We will start from what is ML, when you search for the best Food places in India, it also shows the suggested and similar places right so, this all done by, ML.
Generally in google voice search, if you ask anything, it shows results according to your voice query Right, this is also done by ML, these are very small examples.
I Think you have got, some Idea about ML and it is a subset of Artificial intelligence. Its main goal, is to design future predictions on its own experiences.
For what it works for? It starts machines to create decisions from source data. Instead of programming for some project or work. These works or programs or ML algorithms designed in a direction that, the machines improve themselves. By this, if a new task is given to a machine, it analyzes the old data and successfully completes the new work with old experiences.
a)How Machines Invented?
For example, if we see the Hollywood movies terminator, terminator-Salvation. It explains how machines build themselves and program themselves. So coming to the point the future is in the hands of machines and Human-beings.
Machine learning project or concept is as same as Human behavior and development. Humans born from their ancestors and learned many things till now. In the same way, machine learning will be derived. In factories or in any company, a project will be completed with the help of Humans and machines.
“Amazon prime" is the best example of machine learning”
b)Working Principles of ML:
Generally, ML applications or algorithms get trained by training Data to Design a Model. If we give the latest input to the ML algorithm. It designs output predictions on principles of the sample.
The ML output prediction should be accurate and exact, then only ML algorithm will be moved. If not, it sent for Re-training with big data set.
As a matter of fact, We can see in schools, students get trained by teachers. Now, who is the teacher for the machine, a data-set is a teacher for the machine. Once the model got trained it starts predicting, the decisions when the latest data is given to it.
This is a special model, it learns by observing things and humans. It scans path in the data. It's automatically checks designs and understandings, between data-set by designing clusters. It has an option that it adds labels to the cluster. If we show cars and bikes to it, it can’t say whether it is a car or bike, because both are vehicles. But it separates them.
If we show a route map to the machine, it will find the best way to reach our destination. Or it will fail to show the output. It just acts as a fight or flight hormone in humans. RL predicts the new method for designing and predicting Data.
f)Use Cases in ML:
The best machine learning use cases are, for collaboration ( Cortana), face detection ( mobile camera), security ( Finger Print sensor ), customer service (chat-bot), Sales Optimization(virtual Displays in Malls), Automation (self-Driving cars), Facebook for ML Chat bots.
I think you have got some idea on “what is Machine Learning” and “ML language”, for more in-depth Information, stay tuned for my upcoming “Machine learning Tutorial”. Machine learning is not a new technology, unknowingly we, using it from many years. The future of the world is the combination of Humans and machines.