AWS Certified Data Analytics Specialty 2021 – Hands-On - Machine Learning 101

The video tutorial introduces AWS machine learning services, highlighting their limitations and the importance of understanding machine learning concepts. It explains machine learning as a system that predicts unknown properties based on known attributes, using examples like house pricing, image classification, and fraud detection. The tutorial delves into supervised learning, describing how models are trained with historical data to predict unknown labels. It also covers model evaluation techniques, such as train and test methods, to measure prediction accuracy.

Which of the following is a limitation of the early AWS machine learning services?

• It can only perform linear and logistic regression.

• It cannot handle any type of regression.

• It does not support any form of supervised learning.

• It is only available for image classification.

What is a common application of machine learning in the medical field?

• Designing new pharmaceuticals.

• Automating surgical procedures.

• Classifying biopsy results as malignant or benign.

• Predicting weather patterns.

In supervised learning, what is the term used for the property you are trying to predict?

• Feature

• Attribute

• Label

• Dataset

What is the purpose of a training dataset in supervised learning?

• To test the model's accuracy.

• To provide historical data for building the model.

• To store the final predictions.

• To visualize the data.

What is the main idea behind the train-test split method?

• To only use data that the model has seen before.

• To ensure the model is trained on incorrect data.

• To split data into training and test sets to evaluate model performance.

• To use all data for training.

Which metric is commonly used to measure the accuracy of a machine learning model?

• Root Mean Squared Error (RMSE)

• Mean Absolute Error (MAE)

• Standard Deviation

• Variance

In the context of predicting house prices, what is the 'label'?

• The number of bedrooms

• The location of the house

• The square footage

• The sale price of the house