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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/
课程评论：没有评论
课程名称：Python中的无监督机器学习：隐马尔可夫模型 课程概述： 隐马尔可夫模型（HMM）是学习序列的重要工具，能够处理各种类型的序列数据，如股票价格、语言文字和信用评分等。能够分析这些序列是数据科学工具箱中一项重要技能。本课程将介绍隐马尔可夫模型作为一种历史悠久的机器学习算法，与当下流行的递归神经网络相比，首先让学习者掌握这种经典算法。 本课程作为“无监督机器学习：聚类分析”的后续课程，学习者将学习如何测量随机变量序列的概率分布。此外，课程将探讨如何利用梯度下降法优化隐马尔可夫模型的参数，提供对比于流行的期望最大化算法的替代方案，使用Theano和Tensorflow这两个深度学习库进行操作。 课程内容涵盖隐马尔可夫模型和马尔可夫模型的多种实践应用，包括疾病健康模型、网站用户交互分析、语言模型开发及Google的PageRank算法等。学习者将了解如何利用HMM解决诸如基因如何转化为生物特征这类基本生物学问题。 该课程强调“如何构建和理解”，不仅仅是“如何使用”API，鼓励学习者通过实验来深入理解模型的内部机制。所有材料均可免费下载，并利用Numpy、Matplotlib和少量的Theano进行课程工作。课程适合希望深入学习机器学习算法实现的学习者，掌握更为核心的理解，而不仅仅是记忆事实。 推荐的先决条件包括：微积分、线性代数、概率论，熟悉多元高斯分布和Python编程等基础知识。 希望在课堂上见到你！
The Hidden Markov Model or HMM is all about learning sequences.A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default. In short, sequences are everywhere, and being able to analyze them is an important skill in your data science toolbox.The easiest way to appreciate the kind of information you get from a sequence is to consider what you are reading right now. If I had written the previous sentence backwards, it wouldn't make much sense to you, even though it contained all the same words. So order is important.While the current fad in deep learning is to use recurrent neural networks to model sequences, I want to first introduce you guys to a machine learning algorithm that has been around for several decades now - the Hidden Markov Model.This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you'll learn to measure the probability distribution of a sequence of random variables. You guys know how much I love deep learning, so there is a little twist in this course. We've already covered gradient descent and you know how central it is for solving deep learning problems. I claimed that gradient descent could be used to optimize any objective function. In this course I will show you how you can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm.We're going to do it in Theano and Tensorflow, which are popular libraries for deep learning. This is also going to teach you how to work with sequences in Theano and Tensorflow, which will be very useful when we cover recurrent neural networks and LSTMs.This course is also going to go through the many practical applications of Markov models and hidden Markov models. We're going to look at a model of sickness and health, and calculate how to predict how long you'll stay sick, if you get sick. We're going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We'll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you. HMMs have been very successful in natural language processing or NLP.We'll look at what is possibly the most recent and prolific application of Markov models - Google's PageRank algorithm. And finally we'll discuss even more practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology - how is DNA, the code of life, translated into physical or behavioral attributes of an organism?All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib, along with a little bit of Theano. I am always available to answer your questions and help you along your data science journey.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.See you in class!"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:calculuslinear algebraprobabilityBe comfortable with the multivariate Gaussian distributionPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileWHAT 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)
