Effective Prediction with Machine Learning - Second Edition [Video]

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Effective Prediction with Machine Learning - Second Edition [Video]

Julian Avila

A one-stop solution to quickly program fast Machine Learning algorithms with NumPy and scikit-learn
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Video Details

ISBN 139781789132793
Course Length1 hour and 32 minutes

Video Description

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.

Table of Contents

Machine Learning – High-Performance Algorithm
The Course Overview
NumPy Basics
Loading and Viewing the Iris Dataset
Viewing the Iris Dataset with Pandas
Plotting with NumPy and Matplotlib
Minimal Machine Learning Solution
SVM Classification
Cross-Validation Using Various Algorithms
Classification versus Regression
Pre-Processing Workflow
Creating Sample Data for Toy Analysis
Scaling Data to the Standard Normal Distribution
Working with Categorical Variables
Creating Binary Features and Imputing Missing Values
Pre-Model Workflow
A Linear Model in the Presence of Outliers
Using Gaussian Processes for Regression
Using SGD for Regression
Dimensionality Reduction
Reducing Dimensionality with PCA
Using Decomposition to Classify with DictionaryLearning
Dimensionality Reduction with Manifolds
Testing Methods to Reduce Dimensionality with Pipelines

What You Will 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

Authors

Table of Contents

Machine Learning – High-Performance Algorithm
The Course Overview
NumPy Basics
Loading and Viewing the Iris Dataset
Viewing the Iris Dataset with Pandas
Plotting with NumPy and Matplotlib
Minimal Machine Learning Solution
SVM Classification
Cross-Validation Using Various Algorithms
Classification versus Regression
Pre-Processing Workflow
Creating Sample Data for Toy Analysis
Scaling Data to the Standard Normal Distribution
Working with Categorical Variables
Creating Binary Features and Imputing Missing Values
Pre-Model Workflow
A Linear Model in the Presence of Outliers
Using Gaussian Processes for Regression
Using SGD for Regression
Dimensionality Reduction
Reducing Dimensionality with PCA
Using Decomposition to Classify with DictionaryLearning
Dimensionality Reduction with Manifolds
Testing Methods to Reduce Dimensionality with Pipelines

Video Details

ISBN 139781789132793
Course Length1 hour and 32 minutes
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