|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-machine-learning-using-python/
课程评论：没有评论
课程名称：使用Python进行实用机器学习 课程概述：如果您渴望成为一名机器学习工程师或数据科学家，那么本课程将非常适合您。在本课程中，您将学习机器学习的核心概念、应用场景、数据的角色以及偏差、方差和过拟合的挑战。您还将学习如何选择合适的性能指标、模型评估技术、使用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。课程将指导您构建分类模型、回归模型和聚类模型，并探讨机器学习模型的部署场景和使用案例。 本课程详细涵盖了用于数据科学和机器学习的Python，尤其对Python初学者至关重要。课程大部分为实践内容，通过完整的项目和实例带领您进行探索性数据分析、模型开发、模型优化和模型评估技术。课程广泛使用Numpy和Pandas库进行探索性数据分析，同时还介绍了Marplotlib和Seaborn库用于创建可视化。此外，课程还包括关于深度神经网络的入门课程，并通过一个图像分类实例展示使用TensorFlow和Keras的操作。 课程内容包括： - 机器学习简介 - 机器学习算法类型 - 机器学习的应用场景 - 数据在机器学习中的作用 - 理解培训或学习的过程 - 理解验证和测试 - Python简介 - 设置机器学习开发环境 - Python内部数据结构 - Python语言元素 - Pandas数据结构 - 系列和数据框 - 探索性数据分析（EDA） - 使用房价预测案例研究学习线性回归模型 - 使用信用卡欺诈检测案例研究学习逻辑回归模型 - 评估模型性能 - 精细调优您的模型 - 超参数调优 - 交叉验证 - 通过图像分类项目学习支持向量机（SVM） - 理解决策树 - 使用随机森林理解集成技术 - 使用PCA进行降维 - K均值聚类与客户细分项目 - 深度学习介绍 通过本课程，您将掌握机器学习的实用技能和Python编程能力，为您的职业生涯铺平道路。
Are you aspiring to become a Machine Learning Engineer or Data Scientist? if yes, then this course is for you. In this course, you will learn about core concepts of Machine Learning, use cases, role of Data, 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 build Classification Models using a range of Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Machine Learning 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 Machine LearningTypes of Machine Learning AlgorithmsUse cases of Machine LearningRole of Data in Machine LearningUnderstanding the process of Training or LearningUnderstanding Validation and TestingIntroduction to PythonSetting up your ML Development EnvironmentPython internal Data StructuresPython Language ElementsPandas Data Structure - Series and DataFramesExploratory Data Analysis - EDALearning 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 TuningCross ValidationLearning SVM through an Image Classification projectUnderstanding Decision TreesUnderstanding Ensemble Techniques using Random ForestDimensionality Reduction using PCAK-Means Clustering with Customer Segmentation ProjectIntroduction to Deep Learning
