Tags: python , panda , sqlalchemy , numpy , machine learning , Pandas , ALL

Classification

Dec. 8, 2016

TAGS: Pandas , machine learning

Classification with scikit-learn

Machine Learning Slides from Stanford University certification

Oct. 23, 2016

TAGS: python , machine learning

What is Machine Learning?

Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.

Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications:

Supervised learning and Unsupervised learning.

Linear Regression

Aug. 11, 2016

TAGS: python , numpy , machine learning

Linear Regression

Basic Pandas Data Frames

June 15, 2016

TAGS: python , panda , sqlalchemy

This notebook demonstrating Pandas Data frames it's part of the book "Python Data Science Handbook". by Jake VanderPlas

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