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Top Free Online Machine Learning Courses to Watch Out for in 2021

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The new buzzword shaking the global business arena is machine learning. It's grabbed the public's imagination, conjuring up images of self-learning AI and robots in the future. Machine learning has prepared the path for technical advancements and tools in manufacturing that would have been unthinkable just a few years ago. It drives the breakthrough technologies that sustain our ways of living, from prediction machines to online TV live streaming. If words like deep learning, neural learning, and artificial intelligence spark your interest, we have a great list of free machine learning courses you can begin with right now.


5 Fantastic Practical Machine Learning Resources

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For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning. There seems, however, to be an increasing trend of getting on to the practical from the start, and mixing practice and theory along the way as resources progress. This post presents 5 such resources. Covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks, these resources cover quite a bit of ground. They are also all free, so get reading, get watching, and get coding.


Image Processing Using OpenCV - With Practical Examples

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It is one of the most widely used tools for computer vision and image processing tasks. It is used in various applications such as face detection, video capturing, tracking moving objects, object disclosure, nowadays in Covid applications such as face mask detection, social distancing, and many more. If you want to know more about OpenCV, check this link. If you want to know about Python Libraries For Image Processing then check this Link. If you want to learn Image processing using NumPy, check this link.



A Practical Introduction to Hierarchical clustering from scikit-learn

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Hierarchical clustering is part of the group of unsupervised learning models known as clustering. This means that we don't have a defined target variable unlike in traditional regression or classification tasks. The point of this machine learning algorithm, therefore, is to identify distinct clusters of objects that share similar characteristics by using defined distance metrics on the selected variables. Other machine learning algorithms that fit within this family include Kmeans or DBscan. This specific algorithm comes in two main flavours or forms: top-down or bottom-up.