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DATA SCIENCE AND MACHINE LEARNING WITH PYTHON – HANDS ON!

DATA SCIENCE AND MACHINE LEARNING WITH PYTHON – HANDS ON!


Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python & Spark.

Created by Sundog Education by Frank Kane, Frank Kane
Last updated 5/2017
English
What Will I Learn?
  • Extract meaning from large data sets using a wide variety of machine learning, data mining, and data science techniques with the Python programming language.
  • Perform machine learning on “big data” using Apache Spark and its MLLib package.
  • Design experiments and interpret the results of A/B tests
  • Visualize clustering and regression analysis in Python using matplotlib
  • Produce automated recommendations of products or content with collaborative filtering techniques
  • Apply best practices in cleaning and preparing your data prior to analysis
Requirements
  • You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
  • Some prior coding or scripting experience is required.
  • At least high school level math skills will be required.
  • This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the machine learning and data mining techniques real employers are looking for, including:
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
Size: 3.82G
Content retrieved from: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/.

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