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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-fun-and-easy-using-python-and-keras/
课程评论：没有评论
课程名称：使用Keras的有趣且简单的机器学习指南 课程概述：欢迎参加这个有趣且简单的机器学习课程，使用Python和Keras。如果你对机器学习领域感到好奇，那么这个课程就是为你准备的！我们将带你踏上一段探索机器学习的旅程。每个部分都包含有趣且引人入胜的白板解释，讨论机器学习中的重要概念，同时配有实际的Python实验，增强你对这个庞大而有利可图的数据科学子领域的理解。 为什么选择这门课程？这是一个有效的问题，答案很简单。这是Udemy上唯一一门能够让你在真实数据上实现一些最常见机器学习算法的课程。此外，你还将接触到神经网络（使用H2o框架）和一些最常见的深度学习算法，使用Keras包。我们设计这门课程的目的是让任何想要以简单有趣的方式学习机器学习最前沿的人，不必学习复杂的数学或无聊的解释。每节课的理论讲解都采用独特的白板动画设计，最大限度地提高了课堂参与度，并改善了知识保留。这确保你吸收的内容比观看其他理论视频或阅读教材多得多。 课程内容及结构： - 回归：线性回归、决策树、随机森林回归 - 分类：逻辑回归、K最近邻（KNN）、支持向量机（SVM）和朴素贝叶斯 - 聚类：K均值、层次聚类 - 关联规则学习：Apriori、Eclat - 降维：主成分分析、线性判别分析 - 神经网络：人工神经网络、卷积神经网络、递归神经网络 实用实验结构：你无需任何先前的Python或统计学/机器学习知识即可开始。课程将从介绍一种最基本的统计数据分析模型及其在Python中的实际应用——普通最小二乘（OLS）回归开始。随后，我们将介绍一些最常见的机器学习回归和分类技术，如随机森林、决策树和线性判别分析。除了提供理论基础，实践实验将展示如何在Python中实现这些方法。学生还将介绍常见数据挖掘技术的实际应用，并掌握使用强大的基于Python的机器学习框架Anaconda（Python发行版）。 最后，你将对人工神经网络（ANN）和Keras包有扎实的基础，能够实现深度学习算法，如卷积神经网络（CNN）。深度学习是一个热门话题，掌握这方面的知识将使你在雇主面前更具吸引力。 如果你感到兴奋，那么在这个3小时的课程中，你将学习如何构建许多令人印象深刻的机器学习应用。本课程的根本目的是确保你能够立即在真实数据上应用基于Python的数据科学。无论你的技能水平如何，都可以开始分析自己项目的数据，并通过实际的机器学习案例给潜在雇主留下深刻印象。这是一门实践性强的课程，尽管我们会探讨与数据科学相关的理论概念，但大多数课程会集中在真实数据上的不同技术实现及结果解释上。每节视频后，你将学习到一个新的概念或技术，可以应用到自己的项目中。 立即行动！我们将亲自支持您，确保您在本课程中的体验成功。如果因为任何原因你对课程不满意，Udemy提供30天退款政策，让你无风险参与。点击报名按钮，我们在课堂上见！
Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. So Many Machine Learning Courses Out There, Why This One?This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package. We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject. What you will Learn in this CourseThis is how the course is structured:Regression - Linear Regression, Decision Trees, Random Forest Regression,Classification - Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,Clustering - K-Means, Hierarchical Clustering,Association Rule Learning - Apriori, Eclat,Dimensionality Reduction - Principle Component Analysis, Linear Discriminant Analysis,Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.Practical Lab StructureYou DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers. Excited Yet?So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your machine learning abilities.It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.
