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In the previous tutorial, we covered how to use the K Nearest Neighbors algorithm via Scikit-Learn to achieve 95% accuracy in predicting benign vs malignant tumors based on tumor attributes.…

In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to…

We begin a new section now: Classification. In covering classification, we're going to cover two major classificiation algorithms: K Nearest Neighbors and the Support Vector Machine (SVM). While these two…

We've been learning about regression, and even coded our own very simple linear regression algorithm. Along with that, we've also built a coefficient of determination algorithm to check for the…

Now that we know what we're looking for, let's actually program the coefficient of determination in Python.https://pythonprogramming.nethttps://twitter.com/sentdexhttps://www.facebook.com/pythonprogra...https://plus.google.com/+sentdex

Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression…

Welcome to the 9th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've been working on calculating the regression, or best-fit, line for…

Welcome to the 8th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Where we left off, we had just realized that we needed…

Welcome to the seventh part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Up to this point, you have been shown the value of…

In the previous Machine Learning with Python tutorial we finished up making a forecast of stock prices using regression, and then visualizing the forecast with Matplotlib. In this tutorial, we'll…

In this video, make sure you define the X's like so. I flipped the last two lines by mistake: X = np.array(df.drop(['label'],1))X = preprocessing.scale(X)X_lately = X[-forecast_out:]X = X[:-forecast_out:]To forecast out,…

Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit to our…

We'll be using the numpy module to convert data to numpy arrays, which is what Scikit-learn wants. We will talk more on preprocessing and cross_validation when we get to them…

To begin, what is regression in terms of us using it with machine learning? The goal is to take continuous data, find the equation that best fits the data, and…

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