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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/
课程评论：没有评论
课程名称：数据科学：Python中的监督机器学习 课程概述：近年来，人工智能（AI）和机器学习的复兴带来了许多惊人的成果，例如能够像人类专家一样分析医学图像并预测疾病。谷歌的AlphaGo程序利用深度强化学习击败了围棋世界冠军，机器学习还被用于自动驾驶汽车的编程，未来将彻底改变汽车行业，显著减少车祸发生率。 本课程将介绍机器学习的基本概念，重点学习以下几种算法：K-最近邻（KNN）算法、朴素贝叶斯分类器、决策树算法和感知器算法。KNN是一种简单直观的分类算法，课程中将探讨其优缺点。接着，我们将研究基于概率的朴素贝叶斯分类器及其线性和二次分类器的应用，加快计算速度。决策树是最复杂的算法之一，我们将进行实际实现，帮助加深理解。最后介绍感知器，作为神经网络和深度学习的先导，值得深入学习。 学习完这些算法后，课程将转向更实际的机器学习主题：超参数调整、交叉验证、特征提取、特征选择和多类分类。我们将与深度学习进行比较，理解两者的优缺点。此外，课程还将讨论Sci-Kit Learn库，因为在实际工作中使用经过优化和测试的代码更加有效。 课程中包含免费的学习材料，提供不同操作系统的Python、Numpy和Scipy的安装指南。课程重视“如何构建和理解”，而不仅仅是“如何使用”，并鼓励通过实验来深入理解模型的内部运作。 建议的先修知识包括微积分、概率论（连续和离散分布、联合、边际、条件概率、PDF、PMF、CDF、贝叶斯定理）以及Python编码（条件语句、循环、列表、字典和集合）。课程特点在于每一行代码都进行详细讲解，不浪费时间在不必要的键入上，也不畏惧大学水平的数学，强调算法的重要细节。 总之，如果你希望获得比表面更深入的机器学习理解，这是适合你的课程。
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.It's important to know both the advantages and disadvantages of each algorithm we look at.Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.We'll do a comparison with deep learning so you understand the pros and cons of each approach.We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:calculus (for some parts)probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
