Online data science courses to jumpstart your future.
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MACHINE LEARNING: PREDICTING HOUSE PRICES
In this article the author explains about predicting his house best sale price by using linear regression using python, pandas, sklearn libraries
Linear regression algorithm should be a nice algorithm here, this algorithm will try to find the best linear prediction (y = a + bx1 + cx2 ; y = prediction, x1,x2 = variables). So for example this algorithm can estimate a price per square meter floor space or price per square meter of garden.
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The goal of this tutorial is to create a moving chart that shows the changes in price of a few stock symbols, similar to Google Finance or Yahoo Finance.

Summary of steps
Download and install the HDP Sandbox
Download and install the latest NiFi release
Create a Solr dashboard to visualize the results
Create a new NiFi flow to pull from Google Finance API, transform, and store in HBase and Solr
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This post covers how to perform some basic in-database statistical analysis using SQL.
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Commonly used in Machine Learning, Naive Bayes is a collection of classification algorithms based on Bayes Theorem. It is not a single algorithm but a family of algorithms that all share a common principle, that every feature being classified is independent of the value of any other feature.
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In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm.
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DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.
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Here is a complete tutorial on the regularization techniques of ridge and lasso regression to prevent overfitting in prediction in python

Ridge and Lasso regression are powerful techniques generally used for creating parsimonious models in presence of a ‘large’ number of features. Here ‘large’ can typically mean either of two things:

Large enough to enhance the tendency of a model to overfit (as low as 10 variables might cause overfitting)
Large enough to cause computational challenges. With modern systems, this situation might arise in case of millions or billions of features
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A comprehensive learning path to become a data scientist using Python. Topics include machine learning, deep learning & pandas on Python.
The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis.
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DataCamp's intro to machine learning with R online tutorial teaches you about different machine learning models & tasks. Learn at your own pace today!

This online machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks:
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Cheatsheets on Python, R and Numpy, Scipy, Pandas
Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.
What is TutLinks?

TutLinks.com is a tutorial links collection site. On TutLinks.com users can find any kind of tutorial be it a dance tutorial or cooking recipe or any technology you want to learn. Registered users can also submit their content (video, audio, blog) related to any tutorials or how to articles.

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