Create Your Own Sophisticated Model with Neural Networks [Video]

More Information
Learn
  • Tuning a decision tree
  • Bagging regression with nearest neighbors
  • Tuning an AdaBoost regressor
  • Using SGD for classification
  • Exploring the Perceptron classifier
  • Stack with a neural network
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.

With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. You will focus on Decision Trees and Ensemble Algorithms. Moving forward, you learn to use scikit-learn to classify text and Multiclass with scikit-learn. You will explore various algorithms for classification. You will also look at Naive Bayes model and Label Propagation. Finally, you'll use Neural Networks using different Classifiers and create your own Simple Estimator.

Style and Approach

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

Features
  • Visualize a decision tree with pydot 
  • Implement random forest regression
  • Classify documents with Naive Bayes
  • Create a Simple Estimator
Course Length 1 hour 24 minutes
ISBN 9781789130157
Date Of Publication 27 Mar 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.