Machine Learning with Scikit-learn [Video]

Machine Learning with Scikit-learn [Video]



Learn to implement and evaluate machine learning solutions with scikit-learn
Video
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RRP $124.99
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Video Details

ISBN 13 9781789134780
Course Length 3 hours 21 minutes

Table of Contents

Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods

Video Description

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

Style and Approach

This course is motivated by the belief that you don’t understand something until you can describe it simply. Work through your problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

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What You Will Learn

  • Review fundamental concepts such as bias and variance
  • Extract features from categorical variables, text, and images 
  • Predict the values of continuous variables using linear regression and K Nearest Neighbors 
  • Classify documents and images using logistic regression and support vector machines
  • Create ensembles of estimators using bagging and boosting techniques
  • Discover hidden structures in data using K-Means clustering
  • Evaluate the performance of machine learning systems in common tasks

Authors

Table of Contents

Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods

Video Details

ISBN 139781789134780
Course Length3 hours 21 minutes
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