MACHINE LEARNING

Transfer Learning

Machine Learning

Definition

Transfer learning is the technique of reusing a model trained on one task to solve another related task. Instead of starting from scratch, you start from a model that has already learned general patterns and adapt it to your specific case. It is the reason SMEs can achieve excellent AI results even with limited proprietary data.

Related terms

AI Fundamentals

Fine-Tuning

Fine-tuning is the process of additional training of a pre-trained AI model on domain-specific data. It allows you to specialize a general model for particular tasks, such as classifying business documents or answering questions about your industry. It is more economical and faster than training a model from scratch.

AI Fundamentals

Foundation Model

A foundation model is a large-scale AI model trained on vast and diverse data that can be adapted to multiple specific tasks. GPT-4, Claude, and Gemini are foundation models. Instead of building a model for every need, companies can start from a foundation model and specialize it for their requirements through fine-tuning or prompt engineering.

Machine Learning

Data Augmentation

Data augmentation is the technique of artificially expanding a dataset by applying transformations to existing data. For images: rotations, zoom, lighting variations. For text: paraphrasing, synonyms, back-translation. It is crucial when you have few training data, a common situation for SMEs starting their AI journey.

EXPLORE

More terms in Machine Learning

Supervised Learning

Supervised learning is the ML technique in which the model learns from labeled examples: for each input, the correct output is provided. For example, by showing thousands of emails labeled as 'spam' or 'not spam', the model learns to classify new emails. It is the most common approach in business applications because it produces reliable and interpretable results.

Unsupervised Learning

Unsupervised learning is the ML technique in which the model finds patterns in data without predefined labels. The model autonomously discovers hidden structures such as groups of similar customers (clustering) or data anomalies. It is useful when you don't know what to look for: AI can reveal market segments or anomalous behaviors you hadn't considered.

Reinforcement Learning

Reinforcement learning is an ML technique in which an agent learns by interacting with an environment and receiving rewards or penalties. It is used to optimize complex strategies such as dynamic pricing, logistics routing, and warehouse management. The agent experiments with different actions and learns which strategy maximizes long-term results.

Classification

Classification is the ML task that assigns a categorical label to an input. Examples: classifying emails (spam/not spam), support tickets (urgent/normal/low), documents (invoice/order/contract), or product images (compliant/defective). It is one of the most widespread and immediate uses of AI in SMEs, with fast and measurable ROI.

Regression

Regression is the ML task that predicts a continuous numerical value. Unlike classification which assigns categories, regression estimates quantities: sales forecasting, real estate price estimation, production cost forecasting. It is fundamental for demand forecasting and financial planning in SMEs.

Clustering

Clustering is an unsupervised ML technique that automatically groups similar data together. It is used to segment customers based on purchasing behavior, group similar products, identify patterns in support calls. The model doesn't need to be told how many groups to find (in many algorithms): it discovers them autonomously in the data.

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