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Packt - Machine Learning Fundamentals
TORRENT SUMMARY
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Gain insight into the difference between supervised and unsupervised models
Explore the data using the Matplotlib library
Study popular algorithms, such as K-means, Gaussian Mixture, and Birch
Implement a confusion matrix using scikit-learn
Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM
Visualize errors in various models using matplotlib
About
You'll begin by learning how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. Then, the focus of the course shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve performance of the algorithm by tuning hyperparameters. When it finishes, this course would have given you the skills and confidence to start programming machine learning algorithms.
Features
Explore scikit-learn uniform API and its application into any type of model
Understand the difference between supervised and unsupervised models
Learn the usage of machine learning through real-world examples
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