# Learning Objectives

Course 1 有监督的机器学习:回归与分类
Week 1 机器学习介绍

  • Define machine learning
  • Define supervised learning
  • Define unsupervised learning
  • Write and run Python code in Jupyter Notebooks
  • Define a regression model
  • Implement and visualize a cost function
  • Implement gradient descent
  • Optimize a regression model using gradient descent

# Overview of Machine Learning

Welcome to Machine learning. What is machine learning? You probably use it many times a day without even knowing it.

Anytime you want to find out something like how do I make a sushi roll?

You can do a web search on Google, Bing or Baidu to find out.

And that works so well because their machine learning software has figured out how to rank web pages.

Or when you upload pictures to Instagram or Snapchat and think to yourself, I want to tag my friends so they can see their pictures.

Well these apps can recognize your friends in your pictures and label them as well.

That's also machine learning.

Or if you've just finished watching a Star Wars movie on the video streaming service and you think what other similar movies can I watch?

Well the streaming service will likely use machine learning to recommend something that you might like.

Each time you use voice to text on your phone to write a text message. Hey Andrew, how's it going?

Or tell your phone. Hey Siri play a song by Rihanna, or ask your other phone okay Google show me Indian restaurants near me.

That's also machine learning.

Each time you receive an email titled, Congratulations! You've won a million dollars.

Well maybe you're rich, congratulations. Or more likely your email service will probably flag it as spam.

That too is an application of machine learning.

Beyond consumer applications that you might use, AI is also rapidly making its way into big companies and into industrial applications.

consumer applications

Applications designed for everyday users

In the most common sense, consumer applications are software programs targeted towards and intended to be used by individual consumers in their daily lives. These are the apps you find on your phone or computer, helping you with various tasks like:

  • Communication: messaging, social media, video calls
  • Entertainment: streaming services, gaming, music apps
  • Productivity: email, calendar, to-do lists
  • Shopping: online marketplaces, food delivery, retail apps
  • Finance: banking apps, payment systems, budgeting tools
  • Health and wellness: fitness trackers, meditation apps, healthcare platforms
  • Travel and transportation: booking platforms, navigation apps, ride-hailing services

Consumer applications are typically easy to use, intuitive, and have a user-friendly interface. They focus on providing immediate value and solving personal needs for individual users.

For example, I'm deeply concerned about climate change, and I'm glad to see that machine learning is already hoping to optimize wind turbine power generation.

Or in healthcare, is starting to make its way into hospitals to help doctors make accurate diagnosis.

Or recently at Landing AI have been doing a lot of work, putting computer vision into factories to help inspect if something coming off the assembly line has any defects.

That's machine learning, it's the science of getting computers to learn without being explicitly programmed.

In this course, you learn about machine learning and get to implement machine learning and code yourself.

Millions of others have taken the earlier version of this course, which is of course, that led to the founding of Coursera.

And many learners ended up building exciting machine learning systems or even pursuing very successful careers in AI.

I'm excited that you're on this journey with me.

Welcome and let's get started.