Effective Prediction with Machine Learning - Second Edition [Video]

More Information
Learn
  • Build predictive models in minutes by using scikit-learn
  • Understand the differences and relationships between Classification and Regression
  • Use distance metrics to predict in Clustering
  • Find points with similar characteristics with Nearest Neighbors
  • Use automation and cross-validation to find the best model and focus on it for a data product
About

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.

This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models.

Finally, you'll explore dimensionality reduction with various parameters.

Style and Approach

This course consists of practical videos on scikit-learn that target novices as well as intermediate users. It explores technical issues in depth, covers additional protocols, and supplies many more real-life examples so that you are able to implement scikit-learn in your daily life.

Features
  • Plot different shapes using NumPy and matplotlib, making the visualization more appealing
  • Imputing missing values through various strategies
  • Use Gaussian processes for regression
Course Length 1 hour 32 minutes
ISBN 9781789132793
Date Of Publication 23 Jan 2018

Authors

Julian Avila

Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab.

He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.