The implementation and growth of open source software (OSS) across business functions and geographies has had many positive impacts making it sometimes worth the hidden risks, in particular to help businesses to quickly develop new processes faster. OSS is generally free, making it popular for teams wanting quick results without budget sign-off or the fear […]
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Reinforcement learning is a method of training machine learning models through trial and error and feedback. The model will be given a goal and list of known actions. The model’s input is the measurement of its environment and current state, and output is the model’s action to move between states. Positive or negative reward signals are released
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Machine learning algorithms learn to make predictions or decisions by learning, from historical data, a model of the underlying process connecting inputs (a.k.a. features) and outputs (a.k.a. labels). If this process underlying the data remains unchanged throughout a model’s lifetime then its performance is likely to remain stable over time. However, if the process changes
Decision trees in machine learning are a common way of representing the decision-making process through a branching, tree-like structure. It’s often used to plan and plot business and operational decisions as a visual flowchart. The approach sees a branching of decisions which end at outcomes, resulting in a tree-like structure or visualization. Decision trees
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Whether being used to clean data, surface trends and insights, or make forecasts, machine learning is increasingly becoming an important tool for data analytics. Organisations are processing more data than ever, so naturally want to harness this data to make decisions. Data analytics is the process of gaining insight and actions from data. As organisations gain
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As machine learning gains traction across industries, businesses are finding new and novel uses for the technology. However, as soon as your organization moves beyond a handful of models, management and observability of this growing system can become a significant challenge. This is where the machine learning maturity model plays a part. Without proper monitoring,
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Machine Learning Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a method for predictive modeling in machine learning, in which an algorithm is used to predict continuous outcomes. Solving regression problems is one of the most common applications for machine learning
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Building walls to tear them down Traditional FSI organizations have silos due to regulatory, legal and operating pressures. Business units and functions try to unify across customer journey transformations to stop the short-term fix of stitching processes and data flows together to bridge customer touch points, often unsustainable, carrying higher cost implications further down the
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Artificial intelligence is a broad field of research which seeks to create systems that mimic or even surpass human intelligence. Machine learning is an approach to system or model development which sees the machine learn from the data itself without direct human control. It means an algorithm can reach a level of complexity and flexibility
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AI Bias appears in machine learning in lots of different forms and in a world that is increasingly becoming more reliant on this type of technology, it’s important to understand the most common types of bias. But as a place to start, the important thing to consider is that training a machine learning model is
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