Whether it's recommending movies or helping scientists find breakthrough cures, machine learning is a powerful new tool with untold potential. In the Understanding the basics of machine learning course videos, we'll explore what these technologies are and how they can be applied in real life to help businesses grow.

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Are you interested in machine learning, but not sure where to start? Using real-world examples, you'll learn about important concepts, terminology, and the phases of a machine learning pipeline. Learn how you can unlock new insights and value for your business using machine learning.

Twenty First Century Mantra: Data is New Oil. This is the Data Era. Every day peta bytes of data getting generated by social media, search engines, e-commerce sites and by many other sources. Only fraction of this huge data source is structured data while most of it is unstructured data. As people are saying that Data is the new oil. So basically, we are having a huge resource at our disposal.

During the training process, we calculate how well the model performs and modify parameters of the f(X) so our result is closer to the real values of Y. While we are doing that we calculate the error of our model. We put in the sample, calculate the error based on the real Y value and modify the parameters of the f(X). The error produced this way is called reducible error because it can be minimized and even completely removed (not a good idea btw). The other part of the equation from above is e – irreducible error.

For example, if we have 43 instances of the training set in the node of which 13 belong to one class, while 30 instances belong to a second class. Given that we have only those two classes in the training dataset, we calculate Gini impurity 1 – (13/43)2 – (30/43)2 1 – 0.09 – 0.49 0.42. When the node is "pure" its Gini index is 0. On the other hand, information gain lets us find the best threshold which will reduce this impurity the most. To calculate information gain we need to calculate average impurity and then subtract that from the starting impurity. That is how we know the quality of thresholds that we used.

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In the current tech landscape, developers are expected to have a number of different skills. And many of them do. There are also a lot of different career paths available to developers that use many of their current skills with a slight twist. Database administrators, developer advocates, and machine learning engineers all have one thing in common with all developers: they all know how to code. It doesn't matter which languages are being used, they all understand the core concepts behind writing good code. That's one of the reasons many software developers consider becoming machine learning engineers. With all of the tools and packages available, you don't need to have a deep mathematical background to get accurate results. If you are willing to learn how to use some libraries and get a high-level understanding of the underlying math, you can become a machine learning engineer.

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In the formula above, f(xi) represents the predicted output value for ith example from the input, and b0 and b1 are regression coefficients that represent the y-intercept and slope of the regression line. We want that value to be as close as possible to the real value – y. Thus model needs to learn the values regression coefficients b0 and b1, based on which model will be able to predict the correct output. In order to make these estimates, the algorithm needs to know how bad are his current estimations of these coefficients. At the beginning of the training process, we feed samples into the algorithm which calculates output f(xi) of the current sample, based on initial values of regression coefficients.