Tag: k-means

Learn Data Science

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From GitHub: “A collection of Data Science Learning materials in the form of iPython Notebooks. Associated data sets. The initial beta release consists of four major topics Linear Regression Logistic Regression Random Forests K-Means Clustering Each of the above has at least three iPython Notebooks covering Overview (an exposition of the technique for the math-wary) Data Exploration (the nuts and bolts of real world data wrangling) Analysis (using the technique to … Continue Reading

Learn Data Science

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Learn Data Science is “a collection of Data Science Learning materials in the form of iPython Notebooks” and the “associated data sets.” The following topics are covered, with at least three notebooks including an Overview – an exposition of the technique for the math-wary”, Data Exploration – “the nuts and bolts of real world data wrangling”, and Analysis – “using the technique to get results”. Topics: Linear Regression Logistic Regression … Continue Reading

NIPS (Neural Information Processing Systems) Conference

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From NIPS Conference site: “The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting” Next event:  December 4 – 9, 2017 – Long Beach, CA – SOLD OUT! Videos from NIPS Conference 2016: NIPS 2016 Sessions … Continue Reading

Data Smart: Using Data Science to Transform Information into Insight

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Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that’s done within the familiar environment of a spreadsheet.

Metis Data Science Bootcamp

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Learn Data Science in 12 weeks with 100% in-person instruction from expert data scientists.

Pattern Recognition and Machine Learning

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This textbook is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics, and is aimed at advanced undergraduates or 1st-year PhD students, as well as practitioners.

Programming Collective Intelligence: Building Smart Web 2.0 Applications

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This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet.

Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules

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In this online course, you will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering.

Doing Data Science: Straight Talk from the Frontline

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How can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.