Google ML Engineer Guided Course

This guided course is for learners who want a structured path through Professional Machine Learning Engineer. Start with ordered lessons, answer exam-style questions, review explanations, and use Practice Mode afterward to test retention.

• Follow an ordered Google ML Engineer course path instead of starting with random questions.
• Use source-backed scenarios to connect concepts to practical administration decisions.
• Review why the correct answer fits and why the distractors do not.
• Track course progress on this device and return to the next lesson later.
• Move from guided learning into Practice Mode when you are ready to check retention and speed.

The full course continues in order. These early lessons are crawlable here so you can see the shape of the path before the interactive course loads.

1. Lesson 1: Because the problem involves large-scale pattern de
   Lesson 1 uses Because the problem involves large-scale pattern de to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.1).
2. Lesson 2: The presence of complex, non-linear interactions be
   Lesson 2 uses The presence of complex, non-linear interactions be to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.1).
3. Lesson 3: A reinforcement learning (RL) framework where the a
   Lesson 3 uses A reinforcement learning (RL) framework where the a to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.1).
4. Lesson 7: The prediction target and how churn will be defined
   Lesson 7 uses The prediction target and how churn will be defined to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.2).
5. Lesson 8: Formulate a new ML metric that more closely proxies
   Lesson 8 uses Formulate a new ML metric that more closely proxies to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.2).
6. Lesson 9: Prioritize high Recall (sensitivity) to minimize fa
   Lesson 9 uses Prioritize high Recall (sensitivity) to minimize fa to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.2).
7. Lesson 13: Ground the model with approved policy documents and
   Lesson 13 uses Ground the model with approved policy documents and to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.3).
8. Lesson 14: Integrate a preprocessing pipeline using the Sensit
   Lesson 14 uses Integrate a preprocessing pipeline using the Sensit to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.3).
9. Lesson 15: Historical bias in the training data; audit the dat
   Lesson 15 uses Historical bias in the training data; audit the dat to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.3).
10. Lesson 19: The dataset is imbalanced and metric selection will
    Lesson 19 uses The dataset is imbalanced and metric selection will to connect Defining the Problem with Problem Framing and Use Case Selection (GPMLE-1.4).