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In the past few years, machine learning (ML) has revolutionized the way we do business. A disruptive breakthrough that differentiates machine learning from other approaches to automation is a step away: Read more on this free link
Machine Learning has completely changed the way business is done in the past few years. Machine Learning algorithms have allowed engineers to leverage data without having to explicitly program machines to follow a specific, fixed path for problem-solving. Read more on this free link
An information-driven mindset is one of the success factors for your business. But the data volume doesn’t boil down to just basic processing capacities. That’s where ML algorithms come on stage. What comes even earlier is unsupervised learning that studies the information and makes it meaningful. Read more on this free link
Machine learning is arguably responsible for data science and artificial intelligence’s most prominent and visible use cases. From Tesla’s self-driving cars to DeepMind’s AlphaFold algorithm, machine-learning-based solutions have produced awe-inspiring results and generated considerable hype. Read more on this free link
The effective use of information is one of the prime requirements for any kind of business operation. At some point, the amount of data produced goes beyond simple processing capacities. That’s where machine learning algorithms kick in. Read more on this free link
Supervised learning refers to using a set of input variables to predict the value of a labeled output variable. It requires labeled data (think of this like an answer key that the model can use to evaluate its performance). Read more on this free link
In a general sense, a cluster is a term used for a group of similar objects or people existing or occurring closely together. Usually, within a larger population of similar and dissimilar data objects, clustering focuses on grouping these objects into smaller groups.Today, the concept of clustering is widely applied in business analytics Read more on this free link
Clustering is an unsupervised machine learning technique with a lot of applications in the areas of pattern recognition, image analysis, customer analytics, market segmentation, social network analysis, and more. A broad range of industries use clustering, from airlines to healthcare and beyond. Read more on this free link
Today, businesses can collect data along every point of the customer journey. This information might include mobile app usage, digital clicks, interactions on social media and more, all contributing to a data fingerprint Read more on this free link
Consulting with clients a few years ago, I would have placed quite an emphasis on the need to distinguish between real-time data and right-time data. If your business processes could be served with hourly updates Read more on this free link
Data analytics – the use of data to understand various components of an organization’s effectiveness – is a tremendous part of any company’s engagement in advertising and sales. Businesses use data analytics to inform better decision making, Read more on this free link
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