Supervised Learning with scikit-learn | DataCamp https://www.datacamp.com/courses/supervised-learning-with-scikit-learn At the end of day, the value of Data Scientists rests on their ability to describe the world and to make predictions. Machine Lear…
(转载)林轩田机器学习基石课程学习笔记1 - The Learning Problem When Can Machine Learn? Why Can Machine Learn? How Can Machine Learn? How Can Machine Learn Better? 每个部分由四节课组成,总共有16节课.那么,从这篇开始,我们将连续对这门课做课程笔记,共16篇,希望能对正在看这们课的童鞋有所帮助.下面开始第一节课的笔记:The Learning Problem. 一.What…
Machine Learning Crash Course  |  Google Developers https://developers.google.com/machine-learning/crash-course/ Google's fast-paced, practical introduction to machine learning ML Concepts Introduction to Machine Learning As you'll discover, machine…
Machine Learning by Andrew Ng | Stanford University | Coursera https://www.coursera.org/learn/machine-learning Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has g…
/ 20220404 Week 1 - 2 / Chapter 1 - Introduction 1.1 Definition Arthur Samuel The field of study that gives computers the ability to learn without being explicitly programmed. Tom Mitchell A computer program is said to learn from experience E with re…
对于一个concept class C,如果存在一个算法A和一个多项式poly(.,.,.,.),有对于任意的ε>0.δ>0以及X的任意分布D和任何target concept C,当sample size m>=poly(1/ε,1/δ,n,size(c))时,不等式: 都成立,那么就说这个concept class C是PAC-learnable的. (1).n:x的维度. (2).size(c): O(n):an upper bound on the cost of the com…
Voting classifier 多种分类器分别训练,然后分别对输入(新数据)预测/分类,各个分类器的结果视为投票,投出最终结果: 训练: 投票: 为什么三个臭皮匠顶一个诸葛亮.通过大数定律直观地解释: 一个硬币P(H)=0.51.大数定律保证抛硬币很多次之后,平均得到的正面频数接近\(0.51 \times N\),并且N越大,越接近.那么换个角度,N表示同时掷硬币的人数,即为这边的N个臭皮匠,他们的结果合到一起就得到的是接近真实结果的值. 进一步根据中心极限定理,即二项分布以正态分布为其极…
Intermediate Python for Data Science | DataCamp https://www.datacamp.com/courses/intermediate-python-for-data-science The intermediate python course is crucial to your data science curriculum. Learn to visualize real data with matplotlib's functions…
机器学习 - 维基百科,自由的百科全书 https://zh.wikipedia.org/wiki/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0 机器学习是人工智能的一个分支.人工智能的研究历史有着一条从以“推理”为重点,到以“知识”为重点,再到以“学习”为重点的自然.清晰的脉络.显然,机器学习是实现人工智能的一个途径,即以机器学习为手段解决人工智能中的问题.机器学习在近30多年已发展为一门多领域交叉学科,涉及概率论.统计学.逼近论.凸分析.计算复杂性理论等多门学科.…
学习笔记之scikit-learn - 浩然119 - 博客园 https://www.cnblogs.com/pegasus923/p/9997485.html 3. Model selection and evaluation — scikit-learn 0.20.3 documentation https://scikit-learn.org/stable/model_selection.html#model-selection Accuracy paradox - Wikipedia…