Supervised Machine learning 2018 - OnClick360

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Machine learning is a core area under artificial intelligence Machine learning (ML) allow the computer to learn the data and predict without being programmed by human intervention, hare Machine is referred to model and learning refer to input dataset. Although Machine learning technology is not new, it is now growing fresh momentum as there are so many things to know about ML. Today, machine learning is different from what it used to be in the past. In Past days where programmers code a machine how to solve a problem. Now we are in the era of machine learning where machines are automatically trying to solve problems, by their own, by identifying the trends and patterns in each data set and to predict future problems and their solutions.


4 Different Machine Learning Techniques You Should Recognize

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Previously, we discussed what machine learning is and how it can be used. But within machine learning, there are several techniques you can use to analyze your data. Today I'm going to walk you through some common ones so you have a good foundation for understanding what's going on in that much-hyped machine learning world. If you are a data scientist, remember that this series is for the non-expert. But first, let's talk about terminology.



Exploring Supervised Machine Learning Algorithms

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The main goal of this reading is to understand enough statistical methodology to be able to leverage the machine learning algorithms in Python's scikit-learn library and then apply this knowledge to solve a classic machine learning problem.


Machine Learning Key Terms - myVertica

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Machine Learning Key Terms Posted on Monday, June 4th, 2018 at 3:03 pm. Share this: This blog post was authored by Soniya Shah. Machine learning seems to be everywhere these days – in the online recommendations you get on Netflix, the self-driving cars that hyped in the media, and in serious cases, like fraud detection. Data is a huge part of machine learning, and so are the key terms. Unless you have a background in statistics or data science, it can be confusing to keep all the terminology straight.