Machine learning

Machine learning has revolutionised how we approach complex problems and make data-driven decisions. This remarkable field has found applications in various industries by empowering computers to learn patterns and make predictions. In this blog, we will delve into the fundamentals of machine learning and explore its potential to transform the world.

What is machine learning?

Machine learning is a branch of artificial intelligence that empowers computers to learn from data, make predictions, and automate tasks without explicit programming. It trains algorithms on extensive datasets to identify patterns, extract insights, and enhance decision-making capabilities. By analysing historical data, machine learning models can effectively generalise past experiences to handle new, unseen examples.

What are the different types of machine learning?

Broadly categorised into supervised and unsupervised learning, these two types form the foundation of machine learning techniques. In this brief introduction, we will explore these types and gain a glimpse into how they operate, enabling computers to acquire knowledge and extract insights from data.

Supervised Learning

This approach involves training models on labelled data, where input features are associated with corresponding output labels. Supervised learning algorithms learn to generalise from the labelled examples to accurately predict labels for new, unseen data points.

Unsupervised Learning

Unlike supervised learning, unsupervised learning algorithms deal with unlabelled data. They discover hidden patterns, structures, or relationships within the data without predefined output labels. Clustering and dimensionality reduction are common unsupervised learning techniques.

Reinforcement Learning

In reinforcement learning, an agent learns to make sequential decisions by interacting with an environment. It receives feedback through rewards or penalties, enabling it to optimise its behaviour and achieve a predefined objective.

What are the key aspects of machine learning?

The key aspects of machine learning are interrelated, and finding the right balance is crucial for building accurate and robust machine learning models. Balancing bias and variance while avoiding overfitting and underfitting is a key challenge in machine learning model development. Techniques such as regularisation, cross-validation, and feature engineering can help address these issues and improve the model's generalisation ability. Let's look in detail at the aspects of machine learning that can affect the performance and accuracy of models:

• Neural Network

  A computational model inspired by biological neural networks consisting of interconnected artificial neurons or nodes that process and transmit information.

• Training Data

  Labelled data used to train a machine learning model, including input features and their corresponding output values.

• Test Data

  Data used to evaluate the performance of a trained machine learning model consists of input features without corresponding output values.

• Feature Extraction

  Selecting or transforming raw input data into representative features that machine learning can utilise algorithm.

• Overfitting

  When a model performs well on training data but fails to generalise to new, unseen data due to learning noise or irrelevant patterns.

• Underfitting

  When a model is too simple or lacks complexity, resulting in poor performance on training and test data.

• Cross-Validation

  The technique used to assess model performance by partitioning data into training and validation sets.

• Bias

  A systematic error within a model that causes it to consistently deviate from the true values.

• Variance

  A model trained on different data subsets experiences fluctuation or inconsistency in predictions, known as a variance.

• Regularisation

  Technique to prevent overfitting by adding a penalty term to the loss function, encouraging simpler models.

• Gradient Descent

  Optimisation algorithm used to minimise the loss function by iteratively adjusting model parameters.

• Hyperparameters

  Parameters set before training that affect the learning process and model performance.

How can Infosys BPM help?

Machine learning continues redefining how we tackle complex problems, enabling data-driven decision-making across various sectors. With its ability to learn from data and make accurate predictions, this transformative field holds tremendous potential to shape the future, driving innovation and improving our lives in countless ways.

The expertise and capabilities of Infosys BPM make it an invaluable partner for businesses seeking to leverage the potential of machine learning. With a focus on seamless cross-platform annotation, Infosys BPM's agile operating model combines client-developed tools and open-source or third-party platforms. This ensures the delivery of high-quality annotated data crucial for training machine learning and AI models. By utilising Infosys BPM's annotation services, businesses can enhance the accuracy and effectiveness of their machine learning initiatives, unlocking new insights and driving innovation. Contact us today to explore how our expertise in machine learning can empower your business to thrive in a data-driven world.