The term “machine learning” refers to a methodology for developing artificial intelligence by utilising computer programmes or hardware models that are able to gain knowledge through experience and discover recurring patterns in collections of data.
- An application of artificial intelligence
- The ability to learn and improve automatically as a result of experience
- recognises patterns in data and creates rules rather than traditional programming.
- begins with observations or data, such as direct experiences or instructions to look for data patterns.
Artificial intelligence and machine learning are utilised in practically all of Google’s software applications. Google Photos will display images that are associated with the search terms you entered and will convert similar still images from your albums into short animated videos. When composing emails, Gmail offers suggestions for phrases and full sentences. Examine and mark as spam any messages that you find. Google Assistant is able to take over real-world responsibilities such as scheduling an appointment for a haircut over the phone.
Relationship between machine learning and statistical analysis
Statistical analysis collects and scrutinises the sampled data to identify trends and formalise relationships between variables.
- is a subset of artificial intelligence in the field of computer science.
- is associated with high-dimensional data.
- Takes away the deterministic function f out of the equation:
Input (X) —> Output (Y)
- belongs to the field of mathematics.
- deals with finding a relationship between variables to predict an outcome.
- deals with low-dimensional data.
- tries to estimate the function f:
dependent variable (Y) = f (independent variable) + error function
The process of machine learning
- leverages existing data, images, and videos to train algorithms and models.
- feeds numerous sets of examples called “training sets” into the system.
- The larger the training set, the more accurate the AI system.
- Label each item in a training set as either 0 or 1.
Input data and the expected output are the components that make up labelled training data. After that, the labelled data is processed by an algorithm for machine learning, which analyses the data patterns and figures out a logic based on what it discovers. After that, the learned model is produced by the algorithm, and it is put to use by being applied to the test data sets.
During the phase of testing, the test data is the one that incorporates all of the inputs and produces output that is determined by the logic that was derived from the training data. The test data are categorised by the system according to the patterns that it has learned from the training data. After that, you will be able to make predictions and derive output by employing the logic of the learned model in conjunction with the patterns found in the test data.
Types of machine learning
- Supervised Learning: The programme is provided with training data along with the expected output or rules to categorise this data, also known as labels. The system uses the set of inputs and outputs to predict the output for future, unseen inputs.
- Unsupervised Learning: The algorithm learns from unlabeled data sets; it only uses the input data to train the model, and it expects to detect patterns and anomalies from these input data. This method is mostly used in MRI analysis.
- Semi-supervised Learning: This is when the training data includes some of the desired output. It is a hybrid approach and a combination of supervised and unsupervised learning; it uses a combination of labelled and unlabeled data. An example of semi-supervised learning is when articles are organised into new topics on the web page.
- Reinforcement learning is a type of machine learning that allows a learning system to observe the environment and learn the ideal behavior. The learning system agent observes the environment, selects and takes certain actions, and receives rewards in return or penalties in certain scenarios. The feedback is given back to the system or agent in a loop. when an agent learns the strategy or policy that maximises rewards over time and tries to maximise the cumulative reward.
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