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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-machine-learning-for-data-scientists/
课程评论：没有评论
课程名称：实用机器学习课程（适合数据科学家） 课程概述： 这门课程是针对数据科学家和机器学习工程师的全面人工智能与机器学习入门指南。课程首先界定了人工智能、机器学习、深度学习及其与数据科学的关系，讲解作为人工智能团队成员需要具备的基础知识与沟通能力。同时讨论什么是可行的项目，什么是不可行的，以及定义优秀人工智能项目的标准。 课程内容包括监督学习的基本概念，涵盖机器学习问题的主要成分和不同的解决方案设置。我们将学习线性模型（包括线性回归、逻辑回归和支持向量机SVM）与非线性模型（如多项式回归、核SVM和深度神经网络DNN）。课程提供了一种通用方法，系统性地解决任何机器学习问题，包括数据准备、探索性数据分析（EDA）、模型选择、模型评估、模型设计、微调和正则化。 课程中的一个端到端示例将在Google Colab笔记本中展示这一过程的实现。我们也会探讨机器学习元算法和集成方法，包括投票法、BAGGing、Boosting决策树和随机森林。最后，介绍无监督学习，涵盖降维算法，如流形学习中的局部线性嵌入（LLE）和主成分分析（PCA），以及聚类算法，如K均值。 整个课程将使用Python语言，并利用流行的机器学习库，如scikit-learn、pandas和keras。
This course is a comprehensive introduction to AI and Machine Learning, targeting Data Scientists and Machine Learning engineers. It starts with setting the boundaries of Artificial Intelligence, Machine Learning, Deep Learning, and their relation to Data Science. What is expected as a member an AI team, and how to speak the same language. What is possible and what is not, and what defines a good AI project. The basics of supervised learning are covered, including the main ingredients of the Machine Learning problem, and the different solution setups. We cover both Linear models (Linear Regression, Logistic Regression, Support Vector Machines (SVM)) and Non-linear models (Polynomial Regression, Kernel SVM, Deep Neural Networks (DNN)). A universal approach is given to tackle any ML problem in a systematic way, covering data preparation, Exploratory Data Analysis (EDA), Model selection, Model evaluation, Model design, Fine tuning and Regularization. An end-to-end is given to illustrate this process with code in Google Colab Notebooks. We also cover the Machine Learning Meta algorithms and Ensemble methods: Voting, BAGGing, Boosting Decision Trees and Random Forests. Finally, we introduce unsupervised learning, covering dimensionality reduction algorithms, like Manifold Learning like Locally Linear Embedding (LLE) and Projection methods like Principal Component Analysis (PCA) and Clustering, like K-Means. Throughout the course, Python language is used. Popular Machine Learning libraries are used, like scikit-learn, in addition to pandas and keras.
