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Andrew Ng's Machine Learning Specialization (Course 1): study plan and how long it takes

The DeepLearningAI playlist is 41 videos and 5.2 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.

41 videos · 5.2 hours · DeepLearningAI

DeepLearning.AI's YouTube playlist for the Machine Learning Specialization covers Course 1, Supervised Machine Learning: Regression and Classification. Andrew Ng teaches linear regression, cost functions and gradient descent, multiple features and feature scaling, logistic regression, overfitting and regularization.

Videos are short, usually under 12 minutes, matching the week and lesson structure of the Coursera course.

Who it's for: Complete beginners to machine learning who want the gentlest possible start from Andrew Ng.

How long does it take?

The full playlist is 5.2 hours across 41 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:

Days to finish the playlist by daily watch time and playback speed
Daily timeAt 1xAt 1.5xAt 2x
30 min a day11 days7 days6 days
1 h a day6 days4 days3 days
2 h a day3 days2 days2 days

Make your own schedule

The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.

Open on YouTube Track watched videos

Week-by-week schedule

A sample plan at 30 minutes a day and normal speed, using whole videos so none is split across days. That's 2 weeks in total. For a different pace, start date or speed, use the planner above.

Week 1Videos 1–23 · 2h 51m

  1. Day 1 · 27m

    • 1#1 Machine Learning Specialization [Course 1, Week 1, Lesson 1]3m
    • 2#2 Machine Learning Specialization [Course 1, Week 1, Lesson 1]4m
    • 3#3 Machine Learning Specialization [Course 1, Week 1, Lesson 2]5m
    • 4#4 Machine Learning Specialization [Course 1, Week 1, Lesson 2]7m
    • 5#5 Machine Learning Specialization [Course 1, Week 1, Lesson 2]7m

  2. Day 2 · 28m

    • 6#6 Machine Learning Specialization [Course 1, Week 1, Lesson 2]9m
    • 7#7 Machine Learning Specialization [Course 1, Week 1, Lesson 2]4m
    • 8#8 Machine Learning Specialization [Course 1, Week 1, Lesson 2]5m
    • 9#9 Machine Learning Specialization [Course 1, Week 1, Lesson 3]10m

  3. Day 3 · 16m

    • 10#10 Machine Learning Specialization [Course 1, Week 1, Lesson 3]7m
    • 11#11 Machine Learning Specialization [Course 1, Week 1, Lesson 3]9m

  4. Day 4 · 24m

    • 12#12 Machine Learning Specialization [Course 1, Week 1, Lesson 3]16m
    • 13#13 Machine Learning Specialization [Course 1, Week 1, Lesson 3]9m

  5. Day 5 · 24m

    • 14#14 Machine Learning Specialization [Course 1, Week 1, Lesson 3]6m
    • 15#15 Machine Learning Specialization [Course 1, Week 1, Lesson 4]8m
    • 16#16 Machine Learning Specialization [Course 1, Week 1, Lesson 4]10m

  6. Day 6 · 29m

    • 17#17 Machine Learning Specialization [Course 1, Week 1, Lesson 4]7m
    • 18#18 Machine Learning Specialization [Course 1, Week 1, Lesson 4]9m
    • 19#19 Machine Learning Specialization [Course 1, Week 1, Lesson 4]7m
    • 20#20 Machine Learning Specialization [Course 1, Week 1, Lesson 4]6m

  7. Day 7 · 24m

    • 21#21 Machine Learning Specialization [Course 1, Week 2, Lesson 1]10m
    • 22#22 Machine Learning Specialization [Course 1, Week 2, Lesson 1]7m
    • 23#23 Machine Learning Specialization [Course 1, Week 2, Lesson 1]7m
Week 2Videos 24–41 · 2h 21m

  1. Day 8 · 28m

    • 24#24 Machine Learning Specialization [Course 1, Week 2, Lesson 1]8m
    • 25#25 Machine Learning Specialization [Course 1, Week 2, Lesson 2]7m
    • 26#26 Machine Learning Specialization [Course 1, Week 2, Lesson 2]8m
    • 27#27 Machine Learning Specialization [Course 1, Week 2, Lesson 2]6m

  2. Day 9 · 25m

    • 28#28 Machine Learning Specialization [Course 1, Week 2, Lesson 2]6m
    • 29#29 Machine Learning Specialization [Course 1, Week 2, Lesson 2]3m
    • 30#30 Machine Learning Specialization [Course 1, Week 2, Lesson 2]6m
    • 31#31 Machine Learning Specialization [Course 1, Week 3, Lesson 1]10m

  3. Day 10 · 21m

    • 32#32 Machine Learning Specialization [Course 1, Week 3, Lesson 1]10m
    • 33#33 Machine Learning Specialization [Course 1, Week 3, Lesson 1]11m

  4. Day 11 · 24m

    • 34#34 Machine Learning Specialization [Course 1, Week 3, Lesson 2]12m
    • 35#35 Machine Learning Specialization [Course 1, Week 3, Lesson 2]6m
    • 36#36 Machine Learning Specialization [Course 1, Week 3, Lesson 3]7m

  5. Day 12 · 29m

    • 37#37 Machine Learning Specialization [Course 1, Week 3, Lesson 4]12m
    • 38#38 Machine Learning Specialization [Course 1, Week 3, Lesson 4]8m
    • 39#39 Machine Learning Specialization [Course 1, Week 3, Lesson 4]9m

  6. Day 13 · 14m

    • 40#40 Machine Learning Specialization [Course 1, Week 3, Lesson 4]9m
    • 41#41 Machine Learning Specialization [Course 1, Week 3, Lesson 4]6m

Tips for finishing

  • The YouTube playlist covers Course 1 only; the labs and Courses 2 and 3 are on Coursera, where you can audit for free.
  • At about five hours, you can finish the videos in a week; spend the rest of your time implementing gradient descent yourself.

Frequently asked questions

How long does it take to finish Andrew Ng's Machine Learning Specialization (Course 1)?

The playlist has 41 videos and 5.2 hours of content. At 1 hour a day that's about 6 days at normal speed, or 4 days at 1.5x. At 2 hours a day it's about 3 days.

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