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所在平台: Coursera专项课程 |
课程评论：没有评论
课程名称：机器学习：理论与实践（Python） 课程概述： 本课程将探索经典的监督学习和无监督学习算法，并介绍深度学习主题。你将学习如何利用流行的Python库构建和评估机器学习模型，并比较各算法的优缺点。同时，课程还将帮助你根据数据的特性，合理选择适合的机器学习模型，提升模型性能，包括超参数调整、抽样和正则化等技术的应用。 所学技能： - 深度学习 - Python 编程 - 无监督学习 - 监督学习 - 超参数调优 - 决策树 - 集成学习 - sklearn - 降维 - 聚类分析 - 推荐系统 专业信息： 该专业涵盖监督学习、无监督学习和深度学习基础。学习者将实际应用机器学习算法于真实数据，理解何时及为何使用特定模型，并提升模型性能。课程内容包括线性回归、逻辑回归、KNN、决策树、随机森林、提升方法及支持向量机等。同时，还将学习无监督学习方法，包括降维技术（如PCA）、聚类和推荐系统，并介绍深度学习的基本原理，如模型架构的选择和用Keras构建/训练神经网络。 应用学习项目： 在本专业中，你将构建电影推荐系统、根据RNA序列识别癌症类型、利用CNN进行数字病理学研究、在灾难推文中应用自然语言处理技术，甚至用GANs生成狗的图像。最后，你需要完成一个监督学习、无监督学习和深度学习的最终项目，以展示你的课程掌握程度。 证书信息： 完成课程后可获得可分享证书。课程为100%在线，允许灵活安排学习进度，预计完成时间为4个月，每周建议学习9小时。 适合人群： 中级水平，需具备微积分、线性代数及Python基础知识。 课程链接： 相关课程链接包括监督学习、无监督学习与深度学习的介绍，均由科罗拉多大学博尔德分校提供。
Course Link: https://www.coursera.org/learn/introduction-to-machine-learning-supervised-learning
Name:Introduction to Machine Learning: Supervised Learning
Description:Offered by University of Colorado Boulder. In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied ... Enroll for free.
Course Link: https://www.coursera.org/learn/unsupervised-algorithms-in-machine-learning
Name:Unsupervised Algorithms in Machine Learning
Description:Offered by University of Colorado Boulder. One of the most useful areas in machine learning is discovering hidden patterns from unlabeled ... Enroll for free.
Course Link: https://www.coursera.org/learn/introduction-to-deep-learning-boulder
Name:Introduction to Deep Learning
Description:Offered by University of Colorado Boulder. Deep Learning is the go-to technique for many applications, from natural language processing to ... Enroll for free.
What you will learn
Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics.
Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses.
Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data’s properties.
Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization.
Skills you will gain
Deep Learning
Python Programming
Unsupervised Learning
Supervised Learning
hyperparameter tuning
Hyperparameter
Decision Tree
ensembling
sklearn
Dimensionality Reduction
Cluster Analysis
Recommender Systems
About this Specialization
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In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.
This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Applied Learning Project
In this specialization, you will build a movie recommendation system, identify cancer types based on RNA sequences, utilize CNNs for digital pathology, practice NLP techniques on disaster tweets, and even generate your images of dogs with GANs. You will complete a final supervised, unsupervised, and deep learning project to demonstrate course mastery.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Calculus, Linear algebra, Python
Hours to complete
Approximately 4 months to complete
Suggested pace of 9 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Calculus, Linear algebra, Python
Hours to complete
Approximately 4 months to complete
Suggested pace of 9 hours/week
Available languages
English
Subtitles: English
