Packt – Machine Learning Fundamentals

您将首先学习如何使用 scikit-learn 的语法。您将学习监督模型和无监督模型之间的区别，以及为每个数据集选择合适算法的重要性。您将在 1990 年美国人口普查数据集上应用无监督聚类算法，以发现模式和概况，并探索解决监督机器学习问题的过程。然后，课程的重点转移到监督学习算法上。您将学习实现不同的监督算法并使用 scikit-learn 包开发神经网络结构。您还将学习如何通过调整超参数来执行一致的结果分析以提高算法的性能。

MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
February 27, 2019 | ISBN: 9781789958386 | English
Duration: 40 Lessons (3h 18m) | Size: 3.33 GB

Learn
Understand the importance of data representation
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