吴恩达机器学习系列课程学习记录（一）¶

Supervised Machine Learning: Regression and Classification

About this Course 课程信息¶

官网原文与自制翻译¶

In the first course of the Machine Learning Specialization, you will:

在机器学习专业化的第一门课程中，您将:

Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
在 Python 中使用流行的机器学习库 NumPy 和 scikit-learn 来建立机器学习模型。
Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
建立并训练监督类机器学习模型，用来预测和二元分类任务，包括线性回归和逻辑回归。

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

机器学习专业化是一个基础的在线程序，由 DeepLearning.AI 与斯坦福在线联合创建。

In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

在这个初学者友好的程序中，你将学到机器学习的基础知识，以及如何使用这些技术去来构建真实的 AI 应用程序。

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.

该课程由吴恩达讲授的，他是一名有远见的 AI 专家，曾在斯坦福大学领导关键研究，并在谷歌 Brain，百度，以及 Landing.AI 进行开创性工作，以此推进 AI 领域的发展。

This 3-course Specialization is an updated and expanded 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.

这门由三门课程组成的专业课程，是吴恩达机器学习课程的更新和拓展版本，评分为 4.9 分（满分 5 分），自 2012 年推出以来，有超过 480 万的学习者。

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.)

本课程提供一个现代机器学习的广泛介绍，包括监督类学习（多元线性回归，逻辑回归，神经网络，以及决策树），非监督类学习（聚类，降维，推荐系统），以及一些硅谷用于人工智能和机器学习创新的最佳实践。（评估和调优模型，采用以数据为中心的方法来提高性能等。）

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.

在本专业课程结束后，你将掌握关键概念，并获得快速有力地应用机器学习来挑战现实世界的问题的实用知识。如果你想进入人工智能领域或在机器学习领域有所建树，那么新的机器学习专业化课程会是最好的出发点。

What you will learn 你将学习如下内容¶

Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
在 Python 中使用流行的机器学习库 NumPy 和 scikit-learn 来建立机器学习模型。
Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
建立并训练监督类机器学习模型，用来预测和二元分类任务，包括线性回归和逻辑回归。

Skills you will gain 你将获得如下技能¶

Regularization to Avoid Overfitting 正则化防止模型过拟合
Gradient Descent 梯度下降
Supervised Learning 监督类机器学习
Linear Regression 线性回归
Logistic Regression for Classification 逻辑回归分类

专业词汇¶

Artificial Intelligence 人工智能
Machine Learning 机器学习
Silicon Valley 硅谷
machine learning libraries 机器学习库

Supervised Machine Learning 监督类机器学习¶

classification 分类
binary classification 二元分类
regression 回归
linear regression 线性回归
multiple linear regression 多元线性回归
logistic regression 逻辑回归
neural networks 神经网络
decision trees 决策树

Unsupervised Machine Learning 非监督类机器学习¶

clustering 聚类
dimensionality reduction 降维
recommender systems 推荐系统