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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-data-science-using-python-md/
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课程名称：使用Python的实用数据科学 课程概述：想要成为数据科学家或机器学习工程师吗？如果是，那么本课程非常适合您。在本课程中，您将学习数据科学的核心概念、探索性数据分析、统计方法、数据的作用、Python语言、偏差、方差和过拟合的挑战、选择正确的性能指标、模型评估技术、通过超参数调优和网格搜索交叉验证技术进行模型优化等内容。您将掌握如何使用Python进行详细的数据分析，包括统计技术和探索性数据分析，并使用各种预测建模技术，如分类算法、回归模型和聚类模型。您还将学习部署预测模型的场景和用例。本课程全面深入地讲解了Python在数据科学和机器学习中的应用，是Python初学者必不可少的学习资料。课程内容以实践为主，包含详细的项目和案例，带您完成探索性数据分析、模型开发、模型优化和模型评估等技术。 课程内容包括： 1. 数据科学简介 2. 用例和方法论 3. 数据在数据科学中的作用 4. 统计方法 5. 探索性数据分析（EDA） 6. 理解训练或学习的过程 7. 理解验证与测试 8. Python语言详解 - 数据科学/机器学习开发环境设置 - Python内部数据结构 - Python语言要素 - Pandas数据结构 - 系列和数据框 9. 探索性数据分析（EDA） 10. 学习线性回归模型（房价预测案例研究） 11. 学习逻辑回归模型（信用卡欺诈检测案例研究） 12. 评估模型性能 13. 微调模型 14. 超参数调优以优化模型 15. 交叉验证技术 16. 通过图像分类项目学习支持向量机（SVM） 17. 理解决策树 18. 理解随机森林的集成技术 19. 使用主成分分析（PCA）进行降维 20. K均值聚类与客户细分 21. 深度学习简介 22. 奖励模块：使用ARIMA进行时间序列预测 本课程全面涵盖Numpy和Pandas库在探索性数据分析中的应用，并详细讲解Matplotlib和Seaborn库用于可视化。课程中还包括了使用TensorFlow和Keras进行图像分类的深度神经网络入门课程。
Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you. In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python. Most of this course is hands-on, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques.This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on Image Classification using TensorFlow and Keras. Course Sections:Introduction to Data ScienceUse Cases and MethodologiesRole of Data in Data ScienceStatistical MethodsExploratory Data Analysis (EDA)Understanding the process of Training or LearningUnderstanding Validation and TestingPython Language in DetailSetting up your DS/ML Development EnvironmentPython internal Data StructuresPython Language ElementsPandas Data Structure - Series and DataFramesExploratory Data Analysis (EDA)Learning Linear Regression Model using the House Price Prediction case studyLearning Logistic Model using the Credit Card Fraud Detection case studyEvaluating your model performanceFine Tuning your modelHyperparameter Tuning for Optimising our ModelsCross-Validation TechniqueLearning SVM through an Image Classification projectUnderstanding Decision TreesUnderstanding Ensemble Techniques using Random ForestDimensionality Reduction using PCAK-Means Clustering with Customer Segmentation Introduction to Deep LearningBonus Module: Time Series Prediction using ARIMA
