|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/projects-and-case-studies-on-machine-learning-with-python/
课程评论：没有评论
**Coursera 课程：Python 实战机器学习项目展示** 本课程带您踏上一段沉浸式的机器学习之旅，通过实际项目和案例研究，弥合理论知识与实际应用之间的差距。您将掌握使用 Python 解决机器学习挑战的实践技能。 **课程内容概览：** * **导言（Lecture 1）：** 机器学习案例研究概述，展示机器学习的广泛应用。 * **环境设置（Lecture 2）：** 指导学员完成所有必要的工具、库和配置，为项目实操做好准备。 * **线性回归技术（Lecture 3-8）：** 深入学习各种线性回归方法，包括普通线性回归、多项式回归、向后消除法、鲁棒回归和逻辑回归，并通过实际案例进行实现。（共6讲） * **K-均值聚类与人脸检测（Lecture 10-15）：** 探索聚类算法，包括创建散点图、计算欧氏距离、打印质心值，并将 K-均值应用于人脸检测。（共6讲） * **时间序列分析（Lecture 16-19）：** 学习时间序列建模，了解训练、测试数据，并利用比特币等真实数据分析输出。（共4讲） * **分类技术（Lecture 20-29）：** 全面掌握分类算法，涵盖水果类型分布、逻辑回归、决策树、K近邻、线性判别分析、高斯朴素贝叶斯，并学习绘制决策边界。（共10讲） * **违约预测案例研究（Lecture 30-41）：** 应用所学技能解决真实的违约预测问题，包括问题定义、数据准备、特征工程、变量探索以及使用混淆矩阵和 AUC 曲线进行可视化。（共12讲） 本课程将理论与实践相结合，使您能够熟练掌握 Python 中各种机器学习算法的实现。无论您是初学者还是经验丰富的从业者，都能从中获得宝贵的学习体验。
Welcome to an immersive journey into the world of machine learning through practical projects and case studies. This course is designed to bridge the gap between theoretical knowledge and real-world applications, providing participants with hands-on experience in solving machine learning challenges using Python.In this course, you will not only learn the fundamental concepts of machine learning but also apply them to diverse case studies, covering topics such as linear regression, clustering, time series analysis, and classification techniques. The hands-on nature of the course ensures that you gain practical skills in setting up environments, implementing algorithms, and interpreting results.Whether you're a beginner looking to grasp the basics or an experienced practitioner aiming to enhance your practical skills, this course offers a comprehensive learning experience. Get ready to explore, code, and gain valuable insights into the application of machine learning through engaging projects and case studies. Let's embark on this journey together and unlock the potential of machine learning with Python.Lecture 1: Introduction to Machine Learning Case Studies This section initiates the course with an insightful overview of machine learning case studies. Lecture 1 provides a glimpse into the diverse applications of machine learning, setting the stage for the hands-on projects and case studies covered in subsequent lectures.Lecture 2: Environmental SetUp Get ready to dive into practical implementations. Lecture 2 guides participants through the environmental setup, ensuring a seamless experience for executing machine learning projects. This lecture covers essential tools, libraries, and configurations needed for the hands-on sessions.Lecture 3-8: Linear Regression Techniques Delve into linear regression methodologies with a focus on problem statements and hands-on implementations. Lectures 3-8 cover normal linear regression, polynomial regression, backward elimination, robust regression, and logistic regression. Understand the nuances of each technique and its application through practical examples.Lecture 10-15: k-Means Clustering and Face Detection Explore the intriguing world of clustering with k-Means. Lectures 10-15 guide you through creating scattered plots, calculating Euclidean distances, printing centroid values, and applying k-Means to analyze face detection challenges.Lecture 16-19: Time Series Analysis Uncover the secrets of time series modeling. Lectures 16-19 walk you through the process of creating time series models, training and testing data, and analyzing outputs using real-world examples like Bitcoin data.Lecture 20-29: Classification Techniques Embark on a journey through classification techniques. Lectures 20-29 cover fruit type distribution, logistic regression, decision tree, k-Nearest Neighbors, linear discriminant analysis, Gaussian Naive Bayes, and plotting decision boundaries. Gain a comprehensive understanding of classifying data using different algorithms.Lecture 30-41: Default Prediction Case Study Apply your skills to a real-world scenario of predicting defaults. Lectures 30-41 guide you through defining the problem statement, data preparation, feature engineering, variable exploration, and visualization using confusion matrices and AUC curves.This course provides a holistic approach to machine learning, combining theoretical concepts with practical case studies, enabling participants to master the implementation of various algorithms in Python.
