The Marvels of Machine Learning

The Marvels of Machine Learning

This week the Gap Talent technology team discuss the topic of Machine Learning, where algorithms and data converge to transforming the way we live, work and innovate.

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What is Machine Learning?

Machine learning is a branch within the broader domain of AI and computer science that empowers computers to learn from data and improve their performance over time without being explicitly programmed. Technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.

At its core, machine learning relies on algorithms that enable computers to identify patterns, make predictions, and take actions based on data. The process begins with the collection of relevant data, which serves as the foundation for training the machine learning model. This data can include anything from text and images to numerical values.

The 3 Main Types of Machine Learning:

1. Supervised Learning: ?is defined by its use of labelled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids?overfitting?or?underfitting. This type of learning is commonly used for tasks such as image recognition, speech recognition, and classification problems.

2. Unsupervised Learning: Unsupervised learning involves training a model on an unlabelled dataset, where the algorithm must find patterns and relationships within the data without any guidance. Its ability to discover similarities and differences in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition.

3. Reinforcement Learning: Reinforcement learning differs from both supervised and unsupervised learning by continuously improving its model based on feedback from experiences.? It learns through trial and error.? As an action is taken, the success of the outcome is graded and receives either a positive or negative score. An example of this could be a self-driving car for which getting from one location to another without crashing would receive a positive score.

Machine learning is a dynamic and evolving field that continues to push the boundaries of what computers can achieve. Its ability to learn from data and adapt to new information has led to ground-breaking advancements in technology. As we look to the future, machine learning is poised to play an increasingly integral role in shaping how we interact with and benefit from the digital world.

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