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Machine learning is a field of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed.
Or technically speaking, machine learning is a branch of artificial intelligence that involves developing and implementing algorithms and statistical models. It allows computer systems to automatically learn from data, recognise patterns, make predictions or decisions and improve their performance over time without being explicitly programmed for each specific task. It involves utilising statistical techniques, optimisation algorithms, and computational power to train models using labelled or unlabelled data. This is done to uncover underlying patterns, relationships, and structures in the data and generalise that knowledge to make accurate predictions or decisions on unseen data.
Nowadays, machine learning has become ubiquitous. It is due to the following advantages it has over traditional practices:
- Extremely efficient with problems that require a lot of fine-tuning or have a long set of rules
- Behaves remarkably in fluctuating environments
- It not only handles enormous and complex data efficiently, but also infers a lot of information from it
- It is very time effective, makes a lot fewer errors and makes accurate predictions
Machine learning is used in e-commerce, finance, healthcare, marketing, transportation, manufacturing, language processing, computer vision, energy, and cybersecurity, among other fields, to improve efficiency, make accurate predictions, enhance decision-making, and enable autonomous systems.
Depending upon different criteria, machine learning can be categorised as follows:
Based on human supervision:
- Supervised Learning involves training a model with labelled data to make predictions or classifications.
E.g.: - Unsupervised Learning aims to discover patterns and structures in unlabelled data.
- Semi-Supervised Learning combines both labelled and unlabelled data to train models.
- Reinforcement Learning uses rewards and feedback to enable an agent to learn through interaction with an environment.
Based on generalisation:
- Instance-Based Learning: The system learns the examples by heart and compares new data using a similarity measure.
- Model-Based Learning: The system builds a model using the data and uses the model to predict new data. It makes use of a performance measure.
Based on training:
- Batch Learning involves training a model on the entire dataset at once.
- Online Learning updates the model continuously as new data arrives, allowing for adaptive and incremental updates.
Like all good things in life, machine learning also has its downsides. We can categorise most of them under the following two categories:
Bad Data:
- Insufficient training data can lead to inaccurate predictions
- If the data is unrepresentative of all types of possible outcomes, the predictions turn out to be erroneous
- Poor quality data and missing data is also not very favourable
- Irrelevant features in the dataset can lead to increased data size and possibly even confusion
Bad Algorithm:
- Sometimes the model fits too well to the data, which ironically poses a problem in machine learning
- Similarly, if the model doesn’t fit the data to a certain extent, that is also considered an issue
- Certain types of data work well with specific algorithms due to the spread/distribution of that data.
It is a general rule of thumb that quality data matters more than the algorithm being deployed. So it is advised to spend more time collecting and preparing data.