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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-python-u/
课程评论：没有评论
**Coursera 机器学习与Python课程总结** 本课程旨在教授数据分析和数据科学的核心技能，特别是如何使用Python进行机器学习。学习数据科学和分析的五大理由包括： 1. **提升解决问题的能力**：培养分析性思维，这在职业和日常生活中都极其有用。 2. **高需求技能**：随着数据驱动型业务的增长，数据分析师和数据科学家的需求日益旺盛，其价值将持续攀升。 3. **分析无处不在**：各行各业的数据都亟待挖掘，以改进流程和获得洞察。 4. **日益重要**：海量数据的涌现为企业决策提供了前所未有的机会，数据分析师的价值和就业前景将更加广阔。 5. **跨领域技能**：数据科学融合了计算机科学、商业和数学，并要求良好的沟通能力。 通过学习本课程，您将了解如何利用Python进行机器学习，并能够使用朴素贝叶斯、决策树、K近邻（KNN）、神经网络和线性回归等算法训练预测模型，并进行模型评估。课程内容涵盖了Python编程基础、应用统计学（描述性统计、推断性统计、回归分析）以及数据可视化（条形图、饼图、箱线图、散点图矩阵、Seaborn和Plotly）。 课程与IBM CRISP-DM数据挖掘流程紧密结合，重点关注机器学习在建模和评估阶段的应用。除了本课程，还有“Python编程基础快速入门”、“Python数据处理与统计分析”以及“Python数据处理与高级可视化”等相关课程，完成这些课程并通过考试，即可获得“SVBook认证数据挖掘师（Python）”。 **课程主要内容包括：** * **入门**：Python编程基础和环境设置。 * **数据挖掘流程**：理解CRISP-DM模型。 * **数据准备与理解**：数据下载、读取、基础数据处理。 * **统计建模**： * 简单线性回归 * 构建和预测线性回归模型 * **聚类分析**： * K-Means聚类（Python实现） * 层次聚类（Python实现） * **分类算法**： * 决策树（ID3算法，Python实现） * K近邻（KNN，Python实现） * 朴素贝叶斯（Python实现） * 神经网络（Python实现） * **算法选择与模型评估**： * 如何选择合适的算法 * 模型评估（分类模型和回归模型的Python评估方法） 完成本系列课程后，学员将具备进行数据挖掘项目所需的基础知识和实践能力。
Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. Gain problem solving skillsThe ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life. 2. High demandData Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase. 3. Analytics is everywhereData is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.4. It's only becoming more importantWith the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities. 5. A range of related skillsThe great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities. This is the bite-size course to learn Python Programming for Machine Learning and Statistical Learning. In CRISP-DM data mining process, machine learning is at the modeling and evaluation stage. You will need to know some Python programming, and you can learn Python programming from my "Create Your Calculator: Learn Python Programming Basics Fast" course. You will learn Python Programming for machine learning and you will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course. I have created Applied statistics using Python for the data understanding stage and advanced data visualizations for the data understanding stage and including some data processing for the data preparation stage. You can look into the following courses to get SVBook Certified Data Miner using PythonSVBook Certified Data Miner using Python is given to people who have completed the following courses:- Create Your Calculator: Learn Python Programming Basics Fast (Python Basics)- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation)- Machine Learning with Python (Modeling and Evaluation)and passed a 50 questions Exam. The four courses are created to help learners understand about Python programming basics, then applied statistics (descriptive, inferential, regression analysis) and data visualizations (bar chart, pie chart, boxplot, scatterplot matrix, advanced visualizations with seaborn, and Plotly interactive charts ) with data processing basics to understand more about the the data understanding and data preparation stage of IBM CRISP-DM model. The learner will then learn about machine learning and confusion matrix, which are the modeling and evaluation stages of the IBM CRISP-DM model. Learners will be able to do data mining projects after learning the courses.ContentGetting StartedGetting Started 2Getting Started 3Getting Started 4Data Mining ProcessDownload Data setRead Data setSimple Linear RegressionBuild Linear Regression Model: Train and Test setBuild and Predict Linear Regression ModelsKMeans ClusteringKMeans Clustering in PythonAgglomeration ClusteringAgglomeration Clustering in PythonDecision Tree ID3 AlgorithmDecision Tree in PythonKNN ClassificationKNN in PythonNaive Bayes ClassificationNaive Bayes in PythonNeural Network ClassificationNeural Network in PythonWhat Algorithm to Use?Model EvaluationModel Evaluation using Python for ClassificationModel Evaluation using Python for Regression
