Manual Python Machine Learning

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The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention. This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum.

Introduction To Machine Learning using Python

The reader can be a beginner or an advanced learner. This tutorial has been prepared for the students as well as professionals to ramp up quickly. This tutorial is a stepping stone to your Machine Learning journey. Python community has developed many modules to help programmers implement machine learning.

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In this article, we will be using numpy, scipy and scikit-learn modules. We can install them using cmd command:.

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A better option would be downloading miniconda or anaconda packages for python, which come prebundled with these packages. Follow the instructions given here to use anaconda. Machine learning involves computer to get trained using a given data set, and use this training to predict the properties of a given new data.

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For example, we can train computer by feeding it images of cats and more images which are not of a cat, and tell each time to computer whether a picture is cat or not. Then if we show the computer a new image, then from the above training, computer should be able to tell whether this new image is cat or not.

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Process of training and prediction involves use of specialized algorithms. We feed the training data to an algorithm, and the algorithm uses this training data to give predictions on a new test data.

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It takes a test data, and finds k nearest data values to this data from test data set. Then it selects the neighbor of maximum frequency and gives its properties as the prediction result.

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For example if the training set is:. Now we want to predict flower type for petal of size 2.

Machine Learning with Python Tutorial - Tutorialspoint

So if we decide no. Their frequencies are 2, 3 and 2 respectively. Therefore the neighbor of maximum frequency is 2 and flower type corresponding to it is b. So for a petal of size 2. Here is a python script which demonstrates knn classification algorithm.