AWS Certified Specialty MLS-C01 Amazon Web Services Study Notes

K-means clustering is a machine learning technique that can be used to classify unlabeled data into different groups based on their similarity. It is an unsupervised learning method, which means it does not require any prior knowledge or labels for the data. K-means clustering works by randomly assigning data points to a number of clusters, then iteratively updating the cluster centers and reassigning the data points until the clusters are stable. The result is a partition of the data into distinct and homogeneous groups. K-means clustering can be useful for exploratory data analysis, data compression, anomaly detection, and feature extraction. References:

• K-Means Clustering: A tutorial on how to use K-means clustering with Amazon SageMaker.
• Unsupervised Learning: A video that explains the concept and applications of unsupervised learning.

To count the number of students who are in a class, the solution needs to detect and locate each student in the video frame. Object detection is a computer vision model that can identify and locate multiple objects in an image. To differentiate students who are performing a stretch correctly from students who are performing a stretch incorrectly, the solution needs to measure the location and angle of each student’s arms and legs. Pose estimation is a computer vision model that can estimate the pose of a person by detecting the position and orientation of key body parts. Image classification, OCR, and image GANs are not relevant for this use case. References:

• Object Detection: A computer vision technique that identifies and locates objects within an image or video.
• Pose Estimation: A computer vision technique that estimates the pose of a person by detecting the position and orientation of key body parts.
• Amazon SageMaker: A fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

The model in Experiment 1 had a high variance error because it performed well on the training data (train error = 5%) but poorly on the test data (test error = 8%). This indicates that the model was overfitting the training data and not generalizing well to new data. The model in Experiment 3 had a lower variance error because it performed similarly on the training data (train error = 5.1%) and the test data (test error = 5.4%). This indicates that the model was more robust and less sensitive to the fluctuations in the training data. The model in Experiment 3 achieved this improvement by implementing regularization, which is a technique that reduces the complexity of the model and prevents overfitting by adding a penalty term to the loss function. The model in Experiment 2 had a minimal bias error because it performed similarly on the training data (train error = 5.2%) and the test data (test error = 5.7%) as the model in Experiment 1. This indicates that the model was not underfitting the data and capturing the true relationship between the input and output variables. The model in Experiment 2 increased the number of layers and neurons in the model, which is a way to increase the complexity and flexibility of the model. However, this did not improve the performance of the model, as the variance error remained high. This shows that increasing the complexity of the model is not always the best way to reduce the bias error, and may even increase the variance error if the model becomes too complex for the data. References:

• Bias Variance Tradeoff - Clearly Explained - Machine Learning Plus
• The Bias-Variance Trade-off in Machine Learning - Stack Abuse