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Machine Learning – an investment for the future

Most of us use machine learning every day without even knowing it. From perfectly ranked web pages in a web search to photo recognition on popular social media platforms, machine learning is everywhere, quietly helping you lead a better, more productive life. Arthur Samuel described machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed.”

What is Machine Learning?

A modern, more formal definition of machine learning is the one from Tom Mitchell. According to Tom: “A computer program is to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Considered by many to be a subset of artificial intelligence, machine learning is a method of data interpretation that automates the creation of the analytical model.

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Robot arm AI analyzing mathematics for mechanized industry problem solving

Machine learning approaches

Therefore, machine learning focuses on having machines (computers) provide valuable insights into problems without programming them. Funnily, most of the tools and techniques used to refer to AI are examples of machine learning. Machine learning is classified into three different types according to the approach used to improve the accuracy of predictive models:

  • Supervised learning
    Supervised learning uses labelled data sets to train algorithms that classify data or predict outcomes accurately. In supervised learning, a training set is used to teach your models to yield the desired output. This dataset has corrected and incorrect outputs, which allow your model to learn over time. Supervised learning helps organizations solve multiple real-world problems at scale and are best applied for spam detection, sentiment analysis, weather forecasting and pricing predictions.
  • Unsupervised learning
    Unsupervised learning relies on pattern discovery to help solve clustering association problems. Unsupervised learning is beneficial when subject matter experts cannot single out common properties from a given data set. Unsupervised learning is best suited for clustering, association, and dimensionality reduction. You can see the use of unsupervised learning in market segmentation, image compression, and other applications.
  • Reinforcement learning
    Reinforcement learning is a behavioral learning model. The algorithm used directs the user to the best outcome based on the feedback received from data analysis. In reinforcement learning, the system is trained through trial and error instead of a sample data set. A sequence of successful decisions will reinforce the process as it best solves the problem at hand.
  • Deep learning
    Deep learning incorporates neural networks in successive layers to learn from data iteratively. This method is best if you are learning patterns from unstructured data. Deep learning emulates how the human brain works and can train computers to deal with poorly defined abstractions and problems. Deep learning is beneficial for facial recognition, speech recognition and computer vision.
  • Semi-supervised learning
    As the name suggests, semi-supervised learning works with a part of the input data being labelled. Considered to be ideal for medical imaging, semi-supervised learning can significantly improve accuracy in automated medical imaging.

How can machine learning be applied in the enterprise?

Machine learning makes it possible to leverage algorithms and models to predict outcomes. This has immense value for companies trying to leverage big data to understand changes in customer behavior, preferences, or even satisfaction ratios. As organizations realize that queries are often insufficient to understand complex problems, they also recognize that it’s the hidden patterns and anomalies buried in the data that can make all the difference.

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Business intelligence with machine learning

Machine learning can also help organizations learn more about customer churn (why customers are leaving). This is the single biggest driver of the high adoption rates of machine learning and has helped organizations both big and small find the reasons for churn rates. This led to the adoption of machine learning to analyze customer history, preferences, services used and complaints. Using the correct algorithm, statisticians and data scientists can create a model to predict the changes that can impact buying patterns and revenue.

While traditional business intelligence approaches analyze past data to evaluate and predict trends, machine learning uses a statistical algorithm to create a model that learns and predicts user activity to anticipate how customer buying patterns may change in the future.

Machine learning and data science at orquidea. At orquidea, we understood the need for data science and impactful technologies to transform your business for the better. Our dedicated machine learning teams include data scientists, statisticians, mathematicians, developers and project managers that will help you make the right choice.

Why do we have statisticians, mathematicians and data scientists?

At orquidea, we believe that specialization is necessary for high quality. This is why we have experienced mathematicians, statisticians, and data scientists in our organization to ensure that the right person performs the right job. The core tenants of machine learning are built on mathematical prerequisites. Additionally, statistics forms an essential part of machine learning as most learning algorithms such as Naïve Bayes, Gaussian Mixture Models and Hidden Markov models are based on probability and statistics. Our mathematicians and data scientists work with our statisticians to combine the power of technology, advanced mathematics and statistics in machine learning and data mining.

Our project managers have worked with dozens of machine learning projects and understand what it takes to create a truly remarkable product. With years of experience behind their back, they know how to steer an ML project to completion, success and then scale. If you believe that your organization has a task at hand that can be best solved using ML, you are in the right place. Whether you’re looking to get predictions, estimates and trends at scale or the automatic retrieval, generation and processing of content or an enhanced experience for your customers, orquidea’s got your back.

Schedule an appointment with one of our ML experts today!

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