An introduction to Machine Learning
Definition of Machine Learning: Arthur Samuel, an early American leader in the field of computer gaming and artificial intelligence, coined the term “Machine Learning ” in 1959 while at IBM. He defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed “. However, there is no universally accepted definition for machine learning. Different authors define the term differently. We give below two more definitions.
- Machine learning is programming computers to optimize a performance criterion using example data or past experience . We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data.
- The field of study known as machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Definition of learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P , if its performance at tasks T, as measured by P , improves with experience E.
- Handwriting recognition learning problem
- Task T : Recognizing and classifying handwritten words within images
- Performance P : Percent of words correctly classified
- Training experience E : A dataset of handwritten words with given classifications
- A robot driving learning problem
- Task T : Driving on highways using vision sensors
- Performance P : Average distance traveled before an error
- Training experience E : A sequence of images and steering commands recorded while observing a human driver
Definition: A computer program which learns from experience is called a machine learning program or simply a learning program .
Classification of Machine Learning
Machine learning implementations are classified into four major categories, depending on the nature of the learning “signal” or “response” available to a learning system which are as follows:
A. Supervised learning:
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. The given data is labeled . Both classification and regression problems are supervised learning problems .
- Example — Consider the following data regarding patients entering a clinic . The data consists of the gender and age of the patients and each patient is labeled as “healthy” or “sick”.
B. Unsupervised learning:
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. In unsupervised learning algorithms, classification or categorization is not included in the observations. Example: Consider the following data regarding patients entering a clinic. The data consists of the gender and age of the patients.
As a kind of learning, it resembles the methods humans use to figure out that certain objects or events are from the same class, such as by observing the degree of similarity between objects. Some recommendation systems that you find on the web in the form of marketing automation are based on this type of learning.
To know more about supervised and unsupervised learning refer to: –https://www.geeksforgeeks.org/supervised-unsupervised-learning/.
C. Reinforcement learning:
Reinforcement learning is the problem of getting an agent to act in the world so as to maximize its rewards.
A learner is not told what actions to take as in most forms of machine learning but instead must discover which actions yield the most reward by trying them. For example — Consider teaching a dog a new trick: we cannot tell it what tell it to do what to do, but we can reward/punish it if it does the right/wrong thing.
When watching the video, notice how the program is initially clumsy and unskilled but steadily improves with training until it becomes a champion.
To know more about Reinforcement learning refer to –https://www.geeksforgeeks.org/what-is-reinforcement-learning/.
D. Semi-supervised learning:
Where an incomplete training signal is given: a training set with some (often many) of the target outputs missing. There is a special case of this principle known as Transduction where the entire set of problem instances is known at learning time, except that part of the targets are missing. Semi-supervised learning is an approach to machine learning that combines small labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning and supervised learning.
Categorizing based on required Output
Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:
- Classification: When inputs are divided into two or more classes, the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are “spam” and “not spam”. To know more about Classification, refer to –https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/
- Regression: Which is also a supervised problem, A case when the outputs are continuous rather than discrete. To know more about Classification, refer to –https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/ and https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/.
- Clustering: When a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task. To know more about Classification, refer to –https://www.geeksforgeeks.org/clustering-in-machine-learning/
Machine Learning comes into the picture when problems cannot be solved using typical approaches. ML algorithms combined with new computing technologies promote scalability and improve efficiency. Modern ML models can be used to make predictions ranging from outbreaks of disease to the rise and fall of stocks.
This article is contributed by Siddharth Pandey. If you like GeeksforGeeks and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or if you want to share more information about the topic discussed above.
In a technology driven world, everybody aspires to learn skills that industry demands. You can start with our Machine Learning Self-Paced Course that not only provides you in-depth knowledge of the machine learning topics but introduces you to the real-world applications too. So, what are you waiting for, just start from here.