What is machine learning?
Machine learning is the practice of programming computers to learn from data.
In the above example, the program will easily be able to determine if given are
important or are “spam”. In machine learning, data referred to as called training
sets or examples.
Why machine learning?Let’s assume that you'd like to write the filter program without using machine
learning methods. In this case, you would have to carry out the following steps:
1. In the beginning, you'd take a look at what spam e-mails looks like. You might
select them for the words or phrases they use, like “debit card,” “free,” and so
on, and also from patterns that are used in the sender’s name or in the body of
the email.
2. Second, you'd write an algorithm to detect the patterns that you've seen, and
then the software would flag emails as spam if a certain number of those patterns
are detected.
3. Finally, you'd test the program, and then redo the first two steps again until the
results are good enough.
Read also : What is Artificial Intelligence?
When should you use machine learning?
1. When you have a problem that requires many long lists of rules to find the
solution. In this case, machine-learning techniques can simplify your code and
improve performance.
2. Very complex problems for which there is no solution with a traditional
approach.
3. Non- stable environments’: machine-learning software can adapt to new data.
Types of Systems of Machine Learning
There are different types of machine-learning systems. We can divide them into
categories, depending on whether------
1. Supervised
2. Unsupervised
3. Semi-supervised
4. Reinforcement Learning
• If they can learn incrementally
• If they work simply by comparing new data points to find data points, or can
detect new patterns in the data ,and then will build a model.
Read also : What is Search Engine?
Supervised Learning
In this type of machine-learning system, the data that you feed into thealgorithm, with the desired solution, are referred to as “labels.”
The most important supervised algorithms
- K-nears neighbors
- Linear regression
- Neural networks
- Support vector machines
- Logistic regression
- Decision trees and random forests
Unsupervised Learning
In this type of machine-learning system, you can guess that the data is unlabeled.
The most important unsupervised algorithms
- Clustering: k-means, hierarchical cluster analysis
- Association rule learning: Eclat, apriori
- Visualization and dimensionality reduction: kernel PCA, t-distributed,
PCA
Semi-supervised learning
is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).Reinforcement Learning
Reinforcement learning is another type of machine-learning system. An agent
“AI system” will observe the environment, perform given actions, and then
receive t rewards in return. With this type, the agent must learn by itself. Ties
called a policy.
You can find this type of learning type in many robotics applications that learn
how to walk
Batch Learning
In this kind of machine-learning systems, the system can’t learn incrementally:the system must obtain all the needed data . That means it will require many
resources and a huge amount of time, so it’s always done offline.
Online Learning
This kind of learning is the opposite of batch learning. I mean that, here, thesystem can learn incrementally by providing the system with all the available
data as instances (groups or individually), and then the system can learn on the
fly.
Instance based learning
This is the simplest type of learning that you should learn by heart. By usingthis type of learning in our email program, it will flag all of the emails that were
flagged by users.
Model-based learning
There is another type of learning in which learning from examples allowsconstruction to make predictions.
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