In this chapter, the basics of machine learning are introduced with its key terminologies and its tasks. The different types of tasks that are involved in machine learning are data acquisition, data cleaning, data modeling, and data visualization. These tasks are discussed in this chapter with ...
机器学习(ML)已经成为我们日常生活中的一个常见元素,也是许多科学和工程领域的标准工具。为了优化ML的使用,理解其基本原理是必要的。 这本书接近ML作为科学原理的计算实现。这一原则包括不断调整给定数据生成现…
Alto is a great way to understand the basics of machine learning. This section contains a number of experiments to help understand how Alto works, and introduce the basics of machine learning to others. Covering aspects from simple object recognition, to introducing k-nearest neighbor (kNN) algor...
6. 强化学习 7. 深度学习 8. 自然语言处理 9. 计算机视觉 10. 推荐系统 11. 总结与展望 ## 1. 引言 - 机器学习的定义和重要性 - 机器学习的发展历程 - 当前机器学习的主要应用领域 ## 2. 机器学习简介 - 机器学习的基本概念 - 机器学习与传统算法的对比 - 机器学习的分类方法 ## 3. 监督学习 - ...
Now that you have refreshed your memory about R, we will be talking about the basics of what machine learning is, how it is used today, and what are the main areas inside machine learning. This section intends to provide an overview into machine learning which will help in paving the way...
Deep learning is a subcategory of machine learning inspired by the structure and functioning of a human brain. In recent times, deep learning has gained a lot of traction primarily because of higher computational power, bigger datasets, and better algorithms with (artificial) intelligent learning ab...
We introduce basic machine learning concepts, provide Scikit-learn tutorial, and teach the reader how to train and evaluate machine learning models. We cover regression, classification, clustering and dimensionality reduction. We also discuss overfitting, cross-validation and evaluation using test set.Acc...
Machine Learning may seem difficult to understand and even harder to use but in practice, incorporating machine learning in your workflow can be as easy as a couple of clicks. Machine Learning Made Easy Playlist. Machine Learning with MATLAB. ...
Getting Started with Azure Machine Learning – Part 1 Getting Started with Azure Machine Learning – Part 2 If you’re already exploring machine learning and perhaps know a bit of R, there’s a free webinar happening on Friday Nov. 14 at 9:30am PDT that might be of interest to you: Op...
在概率密度估计过程中,如果我们对随机变量的分布是已知的,那么可以直接使用参数估计的方法进行估计,如最大似然估计方法。 然而在实际情况中,随机变量的参数是未知的,因此需要进行非参数估计。核密度估计是非参数估计的一种方法,也就是大家经常听见的parzen 窗方法了。 本文主要介绍非参数估计的过程以及parzen窗方法估计...