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

The video tutorial introduces machine learning, explaining its basic concepts and real-world applications in fields like real estate, image classification, medicine, and finance. It discusses the intersection of machine learning with big data, highlighting the complexity of algorithms when handling large datasets. The tutorial focuses on supervised learning, detailing how models are trained using historical data to predict unknown labels. It also covers methods to evaluate model accuracy, such as train-test splits and metrics like root mean squared error.

What are the two types of regression mentioned as part of the early machine learning services?

• Decision tree and random forest

• Polynomial and ridge regression

• Support vector and k-nearest neighbors

• Linear and logistic regression

Which of the following is NOT an application of machine learning mentioned in the video?

• Fraud detection

• Weather forecasting

• Image classification

• Predicting house prices

How does machine learning help in the medical field according to the video?

• By managing hospital inventory

• By scheduling doctor appointments

• By diagnosing diseases based on biopsy results

• By predicting patient recovery time

What is the main challenge when combining machine learning with big data?

• Data privacy concerns

• Insufficient computing power

• Complexity of algorithms

• Lack of data

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

• Variable

• Attribute

• Feature

• Label

What is the purpose of splitting data into a training set and a test set?

• To reduce the size of the dataset

• To improve the speed of the algorithm

• To ensure data privacy

• To evaluate the model's performance on unseen data

Which accuracy metric is mentioned in the video for evaluating model performance?

• F1 score

• Mean absolute error

• Root mean squared error

• Precision