In the last several years, the obsession for Machine learning has swept over all technologies and businesses.
Machine learning has become one of the most important tools for companies to solve various kinds of problems. In this series, you will come across a Beginner and Non-technical guide on ML, Coding Deep learning, and differences between AI and ML.
Most information out there in the process of learning the ins-and-outs of machine learning is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. AI and machine learning are fascinating but can be tricky at times. Machine learning is the branch of AI that explores ways to get computers to improve their performance based on experience. There are many different models that can be used in machine learning but they are typically grouped into three different types of learning.
Getting into Machine Learning isn’t an easy thing. The names like Linear Regression, Logistic Regression, and Decision Trees etc. are just the names of the algorithms. Those are just theoretical concepts that describe what to do in order to achieve the specific effect. Model is a mathematical formula which is a result of Machine Learning algorithm implementation. It has measurable parameters that can be used for prediction. Models can be trained by modifying their parameters in order to achieve better results. It is possible to say that models are representations of what a Machine Learning system has learned from the training data.
As big data gradually comes into play in the world of business, machine learning has become one of the most important tools for companies to solve various kinds of problems. Firms across various industries are all trying to incorporate this rising technique into their business and get a competitive edge in understanding their consumers better. Here's taking a look at the basics of machine learning and how your organization can benefit from it.
Introduction Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult.
Introduction Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.
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