What is Machine learning?
Also called: MLDefinition
Machine learning is a branch of artificial intelligence in which computer systems learn patterns from data and use them to make predictions or decisions, instead of following rules written by hand. The main types are supervised learning, which learns from labelled examples; unsupervised learning, which finds structure in unlabelled data; and reinforcement learning, which learns through rewards. Machine learning powers fraud detection, demand forecasting, recommendations and credit scoring, and deep learning, a subset, underpins modern generative AI.
A typical supervised project collects historical data, chooses the target to predict, prepares features, trains a model such as a gradient-boosted tree or neural network, and tests it on data it has not seen. Metrics such as accuracy, precision, recall or error are used to judge whether it is good enough. Once deployed, the model is monitored because real-world data shifts over time.
For businesses, machine learning turns historical data into forward-looking decisions: which customers are likely to churn, which transactions look fraudulent, how much stock to order. Many of these problems involve structured, tabular data, where classical machine learning often performs very well.
Common mistakes include starting without a clear business decision to support, using data that leaks the answer into training, judging models on accuracy alone when classes are imbalanced, and not monitoring after deployment. Clean data and a well-framed problem usually matter more than the choice of algorithm.
Key points
- Learns patterns from data rather than hand-written rules.
- Main types: supervised, unsupervised and reinforcement learning.
- Deep learning is a subset and underpins generative AI.
- Must be tested on unseen data and monitored after deployment.
- Problem framing and data quality matter most.
An example at work
A Bengaluru quick-commerce company trains a machine learning model on past orders, weather and local events to forecast hourly demand for each dark store, reducing guesswork in staffing and stock.
Where this is used at Bodhih
Related terms
Generative AI
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio, video or code, from patterns learnt from data.
Large language model
A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate language by predicting the next token.
Embeddings
Embeddings are numerical vectors that represent the meaning of text, images or other data, so that similar items sit close together mathematically.
Fine-tuning
Fine-tuning is further training of a pre-trained AI model on a smaller, task-specific dataset to adapt its behaviour, style or performance.
AI literacy
AI literacy is the ability to understand what AI can and cannot do, use it effectively and safely, and judge the quality of its output.
More about Machine learning
What is the difference between AI and machine learning?
Artificial intelligence is the broad goal of building systems that perform tasks requiring intelligence. Machine learning is the main method used to achieve that today, where systems learn from data. All machine learning is AI, but not all AI is machine learning; older rule-based expert systems are AI without learning.
Do I need to know coding to learn machine learning?
To understand concepts and work with ML teams, no. To build models yourself, yes: Python is the standard language, along with libraries such as pandas and scikit-learn. Bodhih’s AI/ML Foundations course needs no coding, while the Applied AI/ML Practitioner course teaches hands-on machine learning in Python.