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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-machine-learning-with-python/
课程评论：没有评论
课程名称：应用机器学习与Python 课程概述：如果您对机器学习领域感兴趣，那么这个课程非常适合您！该课程由两位专业的数据科学家设计，旨在以简单易懂的方式分享复杂的理论、算法和编码库。我们将逐步引导您进入机器学习的世界。在每个教程中，您将发展新的技能，并提高对这一具有挑战性但利润可观的数据科学子领域的理解。课程既有趣又令人兴奋，同时我们会深入探讨机器学习的各个方面。 课程结构如下： 第一部分 - 数据预处理 第二部分 - 回归：简单线性回归、多元线性回归、 polynomial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：词袋模型及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、内核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 此外，本课程包含基于真实案例的实际练习。您不仅将学习理论，还可以动手实践构建自己的模型。课程还提供Python和R代码模板，您可以下载并在自己的项目中使用。 重要更新（2020年6月）：所有代码已更新，深度学习代码采用TensorFlow 2.0，涵盖最新的梯度提升模型，包括XGBoost和CatBoost！
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. 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 is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured 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, XGBoostMoreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.Important updates (June 2020):CODES ALL UP TO DATEDEEP LEARNING CODED IN TENSORFLOW 2.0TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
