What Is Machine Learning??
We Discuss in this article about :
We Discuss in this article about :
| What Is Machine Learning ? |
| How Does Machine Learning Work ? |
| Types Of Machine Learning |
| Applications Of Machine Learning. |
What Is Machine Learning?
- So, as we know humans are learn from their past experiences and machines are follow the instructions.
- If machine learn from human how to learn from past experiences Then it is Machine Learning.
- Machine Learning works from on the data development of computer program that can be access data and use it to automatically learn and improve from experiences.
Definition by Tom Mitchell (1998) :
Machine learning is the study of algorithms that
- Improve their performance P
- At some task T
- With experience E.
Examples Of Machine Learning In Day To Day Life :
- Alexa
- Image Recognition
- Speech Recognition
- Medical Diagnosis etc...
How Does Machine Learning Work ?
Machine Learning uses algorithm to mimic human learnings in machines. It is a subset of Artificial Intelligence.
In Machine Learning There is no need to write programs, Computer automatically generates the program.
Work :
- We create a model with trained machine learning algorithm.
- When we gives input to the machine it checks the machine learning algorithm then it creates a prediction and gives output.
- If Output is right then prediction is right and all OK.
- If Output is not right then from the feedback create a further machine learning algorithm and checks till the output not comes right.
- so, this how Machine Learning learns from the mistakes.
For more information we have to know about Types Of Machine learning.
In Machine Learning There is no need to write programs, Computer automatically generates the program.
Work :
- We create a model with trained machine learning algorithm.
- When we gives input to the machine it checks the machine learning algorithm then it creates a prediction and gives output.
- If Output is right then prediction is right and all OK.
- If Output is not right then from the feedback create a further machine learning algorithm and checks till the output not comes right.
- so, this how Machine Learning learns from the mistakes.
Difference between Traditional Programming and Machine Learning :
For more information we have to know about Types Of Machine learning.
Types Of Machine Learning
In Machine Learning machines are used the algorithms and perform on the basis of algorithm and stored input data in the system.
Types :
There Is three part of machine learning :
1) Supervised :
- Develop predictive model based on input and output data.
- Makes Machine learn explicitly.
- Direct Feedback given.
- Predicts outcome.
2) Unsupervised :
- Group and interpret data based only on input data.
- Does not predict.
- Machine Understands the data.
- Evaluation is indirect.
3) Reinforcement :
- It learns from the mistake.
- An approach of AI.
- Machine learn how to act in certain environment.
We will discuss in brief about types of machine learning in next article. this is an overview of types for understanding.
Applications Of Machine Learning
Social Media : Sentiment Analysis, Filtering spam etc..
Genetics : Exposure, Latent defect, haritable pathology etc..
Financial Services : Algorithm trading, Portfolio Management, Fraud detection etc..
Healthcare : Drug Discovery, Disease Diagnosis, Robotic Surgery etc..
Virtual Assistant : Intelligent Agents, Natural Language, Processing etc..
eCommerce : Customer Support, Product Recommendation, Advertising etc..
Transport : Safety, Monitoring, Air traffic control etc..
In Addition Answers For Some Questions Such As :
What Is Machine Learning ?
1) You Can also say that Machine Learning is an extensively algorithm driven study which makes software capable of learning on the basis of experience and improve performance for task.
2) Machine Learning Stands fr learning defined as the acquisition of knowledge or skills through experience, study or by being taught.
3) Machine Learning is the intersection between theoretically sound computer science and practically noisy data.
Essentially, it's about machines making sense out of data in much the same way that we humans do.
4) It is about improving the machine's performance on a specified task with it's experience.
In this article we learn about Machine Learning in short, in next article we discussed in deep about supervised,unsupervised and reinforcement learning with their example.
