Machine learning is a type of artificial intelligence where a machine can learn from data without being explicitly programmed.
Arthur Samuel coined the term machine learning in 1959 and used it to refer to the idea that the computer could be programmed using statistical techniques to learn from examples automatically.
Machine learning works by going through the training data and finding patterns in it. For example, if you have a set of photos with different flowers in them, machine learning would try to find that all photos with roses share common features such as shape and colour. Once ML finds these patterns, it can then be told to look for them in new data later.
This type of AI has been used for things like voice recognition software and content recommendations on the internet. It is also used in image editing software to provide filters for image or video editing projects.
Machine learning is a way to program a machine such that it can perform operations automatically through previous experience or past results. In other words, we train an algorithm with a large data set. Various parameters are organized according to the input values, and outputs are mapped accordingly.
When we feed with real data from the previous optimization and history, it predicts the present values. And the result can be improved by tuning the parameters on the dataset.
We feed a large set of data, these are divided into train data and test data. Our algorithm learns from train data, and we predict the accuracy and precision of our algorithm on test data.
Machine learning is closely related to computational statistics, which makes predictions using computers. It also focuses on the mathematical optimization of methods, theory and application domains to machine learning.
Types of Machine Learning
Machine Learning can be broadly classified into three types:
Supervised learning instructs models to produce the desired output using a training set. This training dataset contains both right and incorrect outputs, allowing the model to improve over time. The algorithm monitors its accuracy using the loss function and adjusts until the error is suitably reduced.
In this, a predefined output is given to the algorithm, such that it distinguishes or maps the values easily. In simple words, outputs are known in the dataset. They, too are of two types:
- Linear Regression: Here values that are being mapped are in a definite range. Linear regression is a statistical technique that is often used to determine the connection between a dependent variable and one or more independent variables to make predictions about future events.
Simple linear regression is used when there is only one independent variable and one dependent variable. Multiple linear regression is used when the number of independent variables rises. It aims to depict the line of greatest fit for each form of linear regression, which is derived using the least-squares approach. However, unlike other regression models, this line is straight when shown on a graph.
- Logistic Regression: While linear regression is appropriate when the dependent variable is continuous, logistic regression is appropriate when the dependent variable is categorical, with binary outputs such as “true” and “false” or “yes” and “no.” While both regression models attempt to comprehend correlations between data inputs, logistic regression is mostly employed to address binary classification issues, such as spam detection.
Unsupervised and supervised machine learning is commonly addressed in the same breath. Unsupervised learning, in contrast to supervised learning, uses unlabeled data. It extracts patterns from the data that aid in resolving grouping or association difficulties. This is especially beneficial when subject matter experts are unclear about a data set’s common qualities. Hierarchical, k-means, and Gaussian mixture models are all popular clustering techniques.
In this learning type, outputs are unknown, and data is provided to the algorithm. It creates clusters within the dataset and identifies them separately such that each cluster has its own unique characteristics.
This type of learning is a combination of both supervised and unsupervised learning, and also they are based on a win or loss system; the machine is simply run on values such that if the specific objective is achieved, then a reward is given, and it learns that otherwise, it try another approach.
For example, if a robot is placed on a table, there is water on one side, and on the other, there is fire. When a robot moves toward the fire, no award is given or loss is found; hence it learns to avoid it in future, and when it moves toward the water, it gets awarded.
Machine Learning has a great scope in future. In real life example, you can see in Gmail (Google App), it automatically marks spam emails and moves them towards trash.
What makes Machine Learning different from Artificial Intelligence?
The two terms are often used interchangeably in the world of machine learning, and there’s a good reason for it. Both AI and Machine Learning make computers do certain things for us that they are not naturally capable of doing, and there are lots of examples of how AI and Machine Learning are alike in this respect. AI means Artificial Intelligence and Machine Learning is another way of saying the same thing, so it is not necessary to differentiate them.
ML enables computers to be able to learn without any human intervention, which would have been impossible to do in the past. The difference between a traditional programmer and a machine learning programmer lies in the types of instructions or program that each are working on, but how exactly they are working on them is fundamentally different. With ML, it is the computer that learns from the data, and then uses the result for more tasks, and learns from that as well. For example, a computer would not be able to recognize the sound of a human voice without a human trainer that tells the computer how to recognize human voices, which is exactly what a ML programmer does, with computers.