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Quick Guide & Tips

💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


🔄   Reset User Workspace

If you need to reset your workspace to its original state, follow these quick steps:

1:   Access the Menu: Look for the three-dot menu (⋮) in the top-right corner of the notebook toolbar.

2:   Restore Original Version: Click on "Restore Original Version" from the dropdown menu.

For more detailed instructions, please visit our Reset Workspace Guide.


💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"


💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

👉👉 🔗 DeepLearning.AI – All Short Courses [+]


🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum


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DeepLearning.AI
/
Machine Learning Specialization
/
  • Course 1
    • Supervised Machine Learning: Regression and ClassificationCourse 1 - 0%
    • Advanced Learning AlgorithmsCourse 2 - 0%
    • Unsupervised Learning, Recommenders, Reinforcement LearningCourse 3 - 0%
  • Week 1
    • Introduction to Machine LearningWeek 1
    • Regression with multiple input variablesWeek 2
    • ClassificationWeek 3
  • All Courses
DeepLearning.AI
/
Machine Learning Specialization
/
  • Course 1
    • Supervised Machine Learning: Regression and ClassificationCourse 1 - 0%
    • Advanced Learning AlgorithmsCourse 2 - 0%
    • Unsupervised Learning, Recommenders, Reinforcement LearningCourse 3 - 0%
  • Week 1
    • Introduction to Machine LearningWeek 1
    • Regression with multiple input variablesWeek 2
    • ClassificationWeek 3
  • All Courses
DeepLearning.AIAll Courses
Machine Learning Specialization
/
  • Course 1
    • Supervised Machine Learning: Regression and ClassificationCourse 1 - 0%
    • Advanced Learning AlgorithmsCourse 2 - 0%
    • Unsupervised Learning, Recommenders, Reinforcement LearningCourse 3 - 0%
  • Week 1
    • Introduction to Machine LearningWeek 1
    • Regression with multiple input variablesWeek 2
    • ClassificationWeek 3
DeepLearning.AI
Machine Learning Specialization
/
  • Course 1
    • Supervised Machine Learning: Regression and ClassificationCourse 1 - 0%
    • Advanced Learning AlgorithmsCourse 2 - 0%
    • Unsupervised Learning, Recommenders, Reinforcement LearningCourse 3 - 0%
  • Week 1
    • Introduction to Machine LearningWeek 1
    • Regression with multiple input variablesWeek 2
    • ClassificationWeek 3

Course Syllabus

Elevate Your Career with Full Learning Experience

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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 the 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 do 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 a 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. 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, it's starting to make its way into hospitals to help doctors make accurate diagnoses. Or recently, at Learning AI, I've 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 class, you learn about machine learning and get to implement machine learning in code yourself. Millions of others have taken the earlier version of this course, which is a 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.
specialization detail
Week 1: Introduction to Machine Learning
    Overview of Machine Learning
  • Welcome to machine learning!
    Video
    ・
    2m
  • Applications of machine learning
    Video
    ・
    4m
  • Supervised vs. Unsupervised Machine Learning
  • What is machine learning?
    Video
    ・
    5m
  • Supervised learning part 1
    Video
    ・
    6m
  • Supervised learning part 2
    Video
    ・
    7m
  • Unsupervised learning part 1
    Video
    ・
    8m
  • Unsupervised learning part 2
    Video
    ・
    3m
  • Jupyter Notebooks
    Video
    ・
    4m
  • Python and Jupyter Notebooks
    Code Example
    ・
    1h
  • Practice Quiz: Supervised vs unsupervised learning
  • Practice quiz: Supervised vs unsupervised learning

    Graded・Quiz

    ・
    15m
  • Regression Model
  • Linear regression model part 1
    Video
    ・
    10m
  • Linear regression model part 2
    Video
    ・
    6m
  • Optional lab: Model representation
    Code Example
    ・
    1h
  • Cost function formula
    Video
    ・
    9m
  • Cost function intuition
    Video
    ・
    15m
  • Visualizing the cost function
    Video
    ・
    8m
  • Visualization examples
    Video
    ・
    6m
  • Optional lab: Cost function
    Code Example
    ・
    1h
  • Practice Quiz: Regression Model
  • Practice quiz: Regression

    Graded・Quiz

    ・
    10m
  • Train the model with gradient descent
  • Gradient descent
    Video
    ・
    8m
  • Implementing gradient descent
    Video
    ・
    9m
  • Gradient descent intuition
    Video
    ・
    7m
  • Learning rate
    Video
    ・
    9m
  • Gradient descent for linear regression
    Video
    ・
    6m
  • Running gradient descent
    Video
    ・
    5m
  • Optional lab: Gradient descent
    Code Example
    ・
    1h
  • Practice quiz: Train the model with gradient descent
  • Practice quiz: Train the model with gradient descent

    Graded・Quiz

    ・
    10m
  • Next
    Week 2: Regression with multiple input variables
  • Certificate
    Course Details