Chapter 2: The Machine Learning Project Cycle & Data Acquisition Technique

Question 1

What is the ML Lifecycle?

Answer 1

Process that guides the development and deployment of machine learning models in a structured way

Question 2

What are the steps in the ML Lifecycle?

Answer 2

1. Acquisition - Collect the data
2. Prepare - Data Cleaning and Quality
3. Process - Run Machine Tools
4. Report - Present the results

Question 3

Process of a ML project?

Answer 3

1. Planning
2. Developing
3. Testing
4. Reporting
5. Refining
6. Production
7. Avoiding Bias

Question 4

What is Transfer Learning?

Answer 4

Technique in ML where a model is trained on one task is used as the starting point for a model on a second task.

Question 5

What is Data Acquisition in Machine Learning?

Answer 5

Procedure of obtaining and compiling data from diverse sources in order to test and train machine learning models.

Question 6

Data Acquisition Techniques?

Answer 6

Scraping Data
Copy and Paste
Data Migration
Using an API

Question 7

Scraping Data

Answer 7

1. Figure out where the data is coming from.
2. Figure out how you’re going to get it.
3. Make it machine readable.
4. Make sure the values are workable.
5. Figure out where to store it.