What is Machine Learning ?
It is a field of artificial intelligence and computer gaming, coined the term Machine Learning. We can also say ML is a subset of Artificial intelligence. In other words, ML is a type of artificial intelligence that take out patterns out of raw data by using an algorithm or method.
WELL-POSED LEARNING PROBLEMS :
If performance of machine as a result with relevancy to some task (T), that’s measured by P, improves with
experience E, then machine is purported to be learning.
Any of the problems can be divided into Three factors
- Tasks
- Measurement of performance
- Experience or source of experience
To give an idea for instance, A child learning to identify the things(like apple ,car, shapes etc) :
Task T: identify the things .
Performance measure P: percent of things correctly classified.
Training experience E: practicing to identify things by remembering the past data .
A checkers learning problem:
- Task T: Taking part in checkers.
- Performance measure P: % of games won against opponents.
- Training experience E: taking part in follow games against itself . .
Once to create Machines Learning ?
Certainly the question arises that in what scenarios we must make the machine learn? There can be several situation in addition where we need machines to take data-driven decisions with efficiency and at a huge scale.
The following are some situation were Machines can learn effectively.
- Lack of human knowledge
- Dynamic scenarios
- Difficulty in translating knowledge into computational tasks.
Challenges in Machines Learning
- Quality of data
- Time-Consuming task
- Lack of specialist persons
- No clear objective for formulating business problems
- Issue of overfitting & underfitting
- Curse of dimensionality
- Difficulty in deployment
Applications of Machine Learning
Machine Learning is that subsequently the most rapidly growing technology and consistent with researchers we tend to area unit within the golden year of AI and ML . Moreover, It’s accustomed solve several real-world complicated issues that can not be solved with ancient approach.
Following area unit some real-world applications of ML
- Emotion analysis
- Sentiment analysis
- Error detection and prevention
- Weather forecasting and prediction
- Stock market analysis and forecasting
- Speech synthesis
- Speech recognition
- Customer segmentation
- Object recognition
- Fraud detection
- Fraud prevention
- Recommendation of products to customer in online shopping.
In 1959, Arthur Samuel outlined Machine Learning as a “Field of study that offers computers the power to be told while not being expressly programmed”.
