|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-a-ztm-ai-python-and-mlops/
课程评论：没有评论
课程名称：从基础到部署的机器学习A-Z 概述：对机器学习领域感兴趣吗？那这个课程正适合你！本课程由数据科学家和机器学习专家设计，旨在以简单的方式分享复杂的理论、算法和编码库。全球超过90万名学生信任该课程。我们将逐步带你进入机器学习的世界。通过每个教程，你将掌握新技能，深化对这一挑战性且利润丰厚的数据科学子领域的理解。你可以选择学习Python教程、R教程或两者都学习——根据你的职业需求选择适合的编程语言。课程内容丰富有趣，深入探讨机器学习，结构如下： 第一部分 - 数据预处理 第二部分 - 回归：简单线性回归、多重线性回归、多项式回归、SVR、决策树回归、随机森林回归 第三部分 - 分类：逻辑回归、K-NN、SVM、核SVM、朴素贝叶斯、决策树分类、随机森林分类 第四部分 - 聚类：K均值、层次聚类 第五部分 - 关联规则学习：Apriori、Eclat 第六部分 - 强化学习：上置信界、汤普森采样 第七部分 - 自然语言处理：词袋模型和NLP算法 第八部分 - 深度学习：人工神经网络、卷积神经网络 第九部分 - 降维：PCA、LDA、核PCA 第十部分 - 模型选择与提升：k折交叉验证、参数调优、网格搜索、XGBoost 每个部分内的内容是独立的。你可以选择从头到尾完成整个课程，也可以直接跳到任何特定部分，学习当前职业所需的内容。此外，课程中包含大量基于真实案例的实践练习。这样你不仅能学习理论，还能在构建模型的过程中获得丰富的动手实践。课程还提供Python和R代码模板，方便你在项目中下载并使用。
Interested in the field of Machine Learning? Then this course is for you!This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries simply.Over 900,000 students worldwide trust this course.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course can be completed by either doing either the Python tutorials, R tutorials, or both - Python & R. Pick the programming language that you need for your career.This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured in the following way:Part 1 - Data PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your models.This course includes both Python and R code templates which you can download and use on your projects.
