|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-regression-and-classification-math-inc/
课程评论：没有评论
Coursera 机器学习：回归与分类 (数学启蒙) 课程总结 本课程主要讲解机器学习中的回归与分类算法，强调在数据科学中算法的作用以及如何通过“训练”大数据来提升决策和预测的准确性。课程内容涵盖了机器学习的广泛应用，例如医疗影像分析、AlphaGo 的成功以及自动驾驶汽车的潜力，旨在展示移除人为错误以降低事故率的未来愿景。 **课程要点概览：** * **决策树：** 探讨了信息增益和 GINI 杂质的概念。课程包含相关的数值问题讲解和代码实现环节。 * **回归树：** 涵盖回归树的实现。 * **线性回归：** 详细介绍了简单线性回归、多元线性回归和多项式线性回归。课程内容包括成本函数、普通最小二乘法 (Ordinary Least Squares, OLS) 的数值实现，以及从零开始编写多变量线性回归代码。 * **梯度下降：** 讲解了梯度下降算法。 * **逻辑回归：** 深入探讨了逻辑回归的决策边界、成本函数和梯度下降等内容，并提供了代码实现。 本课程理论与实践相结合，通过概念讲解、数值问题解决和代码实践，帮助学习者掌握机器学习中的核心回归与分类技术。
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Topics covered in this course:1. Lecture on Information Gain and GINI impurity [decision trees]2. Numerical problem related to Decision Tree will be solved in tutorial sessions3. Implementing Decision Tree Classifier in workshop session [coding]4. Regression Trees 5. Implement Decision Tree Regressor 6. Simple Linear Regression 7. Tutorial on cost function and numerical implementing Ordinary Least Squares Algorithm8. Multiple Linear Regression9. Polynomial Linear Regression 10. Implement Simple, Multiple, Polynomial Linear Regression [[coding session]]11. Write code of Multivariate Linear Regression from Scratch12. Learn about gradient Descent algorithm13. Lecture on Logistic Regression [[decision boundary, cost function, gradient descent.....]]14. Implement Logistic Regression [[coding session]]
