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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-aws-sagemaker-6-real-world-case-studies/
课程评论：没有评论
课程名称：AWS SageMaker 实践入门：构建 6 个项目 概述：本课程更新于2021年4月22日，新增了关于AWS SageMaker Autopilot的案例研究；更新于2021年4月23日，修正了代码脚本和Q&A问题。机器学习和深度学习是当前技术领域的热门话题，已在银行、医疗、交通等多个行业得到应用。AWS是全球最广泛使用的机器学习云计算平台之一，众多财富500强公司依赖AWS进行业务操作。SageMaker是AWS内一个完全托管的服务，允许数据科学家和人工智能从业者快速高效地训练、测试和部署AI/ML模型。 在本课程中，学生将学习如何使用AWS SageMaker创建AI/ML模型，项目涵盖商业、医疗和科技等多个主题。学生将以实践的方式掌握许多主题，例如：1）数据工程和特征工程 2）AI/ML模型选择 3）选择适当的AWS SageMaker算法以解决业务问题 4）AI/ML模型的构建、训练和部署 5）模型优化和超参数调优。 课程涵盖多个主题，包括数据工程、AWS服务与算法、机器学习和深度学习基础：数据工程：数据类型、关键的Python库（pandas、Numpy、scikit Learn、MatplotLib、Seaborn）、数据分布与特征工程（填补、分箱、编码和标准化）。AWS服务和算法：Amazon SageMaker、线性学习者（回归/分类）、Amazon S3存储服务、梯度提升树（XGBoost）、图像分类、主成分分析（PCA）、SageMaker Studio和AutoML。机器与深度学习基础：人工神经网络类型、激活函数、机器学习训练策略、梯度下降算法、正则化、过拟合、特征检测器、决策树等。 课程包括实践项目：项目1：使用AWS SageMaker线性学习者训练、测试和部署简单回归模型以预测员工薪资。项目2：训练、测试和部署多个线性回归模型以预测医疗保险费用。项目3：使用XGBoost回归训练、测试和部署模型以预测零售店销售并优化模型超参数。项目4：使用SageMaker内置PCA算法进行降维，构建分类器模型以预测心血管疾病。项目5：开发使用SageMaker和Tensorflow的交通信号分类器模型。项目6：深入了解AWS SageMaker Studio、AutoML和模型调试。 本课程面向希望深入理解AWS SageMaker并解决现实世界挑战问题的初学者开发人员和数据科学家。建议具备机器学习、Python编程和AWS云的基础知识。此课程适合以下人群：初学者数据科学家希望提升职业生涯并建立个人作品集；希望利用AI/ML通过SageMaker转型业务的资深顾问；热爱技术且对数据科学与AI感兴趣的技术爱好者，想要获得使用AWS SageMaker的实践经验。 立即报名，期待在课堂见到你！
# Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot. # Update 23/04/2021 - Updated code scripts and addressed Q & A bugs. Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.AWS is one of the most widely used ML cloud computing platforms worldwide - several Fortune 500 companies depend on AWS for their business operations.SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.In this course, students will learn how to create AI/ML models using AWS SageMaker. Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning.The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way:Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML.Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent), machine learning training strategies (supervised/ unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, feature detectors, pooling, batch normalization, vanishing gradient problem, confusion matrix, precision, recall, F1-score, root mean squared error (RMSE), ensemble learning, decision trees, and random forest.We teach SageMaker's vast range of ML and DL tools with practice-led projects. Delve into:Project #1: Train, test and deploy simple regression model to predict employees' salary using AWS SageMaker Linear LearnerProject #2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium.Project #3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker Hyperparameters tuning tool.Project #4: Perform Dimensionality reduction Using SageMaker built-in PCA algorithm and build a classifier model to predict cardiovascular disease using XGBoost Classification model.Project #5: Develop a traffic sign classifier model using Sagemaker and Tensorflow.Project #6: Deep Dive in AWS SageMaker Studio, AutoML, and model debugging.The course is targeted towards beginner developers and data scientists wanting to get fundamental understanding of AWS SageMaker and solve real world challenging problems. Basic knowledge of Machine Learning, python programming and AWS cloud is recommended. Here's a list of who is this course for:Beginners Data Science wanting to advance their careers and build their portfolio.Seasoned consultants wanting to transform businesses by leveraging AI/ML using SageMaker.Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience using AWS SageMaker.Enroll today and I look forward to seeing you inside.
