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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/machine-learning-introduction
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课程名称：机器学习 课程概述： 本课程将教您如何使用 NumPy 和 scikit-learn 构建机器学习模型，训练监督模型以进行预测和二分类任务（线性回归、逻辑回归）。您还将学习如何使用 TensorFlow 构建和训练神经网络以进行多类分类，创建决策树和树集成方法。课程涵盖最佳实践以及无监督学习技术，如聚类和异常检测，并教您如何构建推荐系统，包括协同过滤方法和基于内容的深度学习方法，最终构建深度强化学习模型。 通过本课程，您将掌握以下技能： - 决策树 - 人工神经网络 - 逻辑回归 - 推荐系统 - 线性回归 - 正则化以避免过拟合 - 梯度下降 - 监督学习 - 用于分类的逻辑回归 - Xgboost - TensorFlow - 树集成方法 关于本专门化课程： 本课程由 DeepLearning.AI 和斯坦福大学在线合作创建，是一门面向初学者的基础在线项目。它涵盖了现代机器学习的广泛主题，包括监督学习（多元线性回归、逻辑回归、神经网络和决策树）以及无监督学习（聚类、降维和推荐系统）。此外，课程还介绍了硅谷在人工智能和机器学习创新中使用的一些最佳实践（如评估和调整模型、采用以数据为中心的方法等）。 课程结束时，您将掌握关键概念，并具备快速有效地将机器学习应用于现实世界问题的实际能力。如果您希望进入人工智能领域或在机器学习方面建立职业生涯，这一新课程是最佳起点。 应用学习项目： 完成本课程后，您将能够： - 使用常见机器学习库 NumPy 和 scikit-learn 在 Python 中构建机器学习模型。 - 构建和训练用于预测和二分类任务的监督机器学习模型，包括线性回归和逻辑回归。 - 使用 TensorFlow 构建和训练神经网络进行多类分类。 - 应用机器学习开发中的最佳实践，以确保您的模型能够适应现实世界的数据和任务。 - 构建和使用决策树及树集成方法（随机森林和提升树）。 - 使用无监督学习技术（包括聚类和异常检测）。 - 使用协同过滤和基于内容的深度学习方法构建推荐系统。 - 构建深度强化学习模型。 课程特点： - 100% 在线学习，灵活的学习时间安排 - 初级课程，需具备基础编程知识（如循环、函数、条件语句）和高中水平的数学知识（算术、代数） - 完成课程大约需时两个月，建议每周学习 8 小时 - 语言: 英语，提供英文字幕 如需了解更多信息，请访问课程链接：[机器学习课程](https://www.coursera.org/learn/machine-learning)。
Course Link: https://www.coursera.org/learn/machine-learning
Name:Supervised Machine Learning: Regression and Classification
Description:Offered by Stanford University and DeepLearning.AI. In the first course of the Machine Learning Specialization, you will: • Build machine ... Enroll for free.
Course Link: https://www.coursera.org/learn/advanced-learning-algorithms
Name:Advanced Learning Algorithms
Description:Offered by Stanford University and DeepLearning.AI. In the second course of the Machine Learning Specialization, you will: • Build and train ... Enroll for free.
Course Link: https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning
Name:Unsupervised Learning, Recommenders, Reinforcement Learning
Description:Offered by Stanford University and DeepLearning.AI. In the third course of the Machine Learning Specialization, you will: • Use unsupervised ... Enroll for free.
What you will learn
Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
Skills you will gain
Decision Trees
Artificial Neural Network
Logistic Regression
Recommender Systems
Linear Regression
Regularization to Avoid Overfitting
Gradient Descent
Supervised Learning
Logistic Regression for Classification
Xgboost
Tensorflow
Tree Ensembles
About this Specialization
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The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Applied Learning Project
By the end of this Specialization, you will be ready to:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.
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.
Beginner Level
Beginner Level
Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)
Other math concepts will be explained
Hours to complete
Approximately 2 months to complete
Suggested pace of 8 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.
Beginner Level
Beginner Level
Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)
Other math concepts will be explained
Hours to complete
Approximately 2 months to complete
Suggested pace of 8 hours/week
Available languages
English
Subtitles: English
