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Machine Learning With Python Tutorial

Machine Learning With Python Tutorial

Simply Easy Learning

   Formats - PDF

   Pages - 159,      ISBN - TP00340

   Development, Data Science and AI ML, Machine Learning

  Language - English

   Published on 01/2016

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Description

Python is a general-purpose high level programming language that is being increasingly used in data science and in designing machine learning algorithms. This tutorial provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real world problems.

This tutorial starts with an introduction to machine learning and the Python language and shows you how to setup Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.

This tutorial also provides various projects that teaches you the techniques and functionalities such as news topic classification, spam email detection, online ad clickthrough prediction, stock prices forecast and other several important machine learning algorithms.

Audience

This tutorial has been prepared for professionals aspiring to learn the basics of Python and develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this tutorial, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language, Python and its packages. After completing this tutorial, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

Prerequisites

Before you start proceeding with this tutorial, we assume that you have a prior exposure to Python, Numpy, pandas, scipy, matplotlib, Windows and any of the Linux operating system flavors. If you are new to any of these concepts, we recommend you to take up tutorials concerning these topics, before you dig further into this tutorial.

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