Introduction to Machine Learning with Sound Cognitive Class Exam Quiz Answers

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Apply Here: Introduction to Machine Learning with Sound Cognitive Class Exam Quiz Answers

Introduction to Machine Learning with Sound Cognitive Class Certification Answers

Lab 1: Gather and Prepare the Data

Question 1: Data gathering is a key component in machine learning.

• True
• False

Question 2: For machine learning models, data needs to be quantifiable and not comparable.

• True
• False

Question 3: Audio files can be compared directly.

• True
• False

Question 4: Truncating audio files to the same length makes them compatible.

• True
• False

Question 5: Silence detection can be used to locate the start of a tune or noise even though the noise might already be any number of bars into a tune.

• True
• False

Lab 2: Build a Machine Learning Model

Question 1: In Watson Studio, which type of project do you create for a machine learning project to make it easy to find associated assets and models?

• Watson tools
• Data science
• Business Analytics
• Streams Designer
• Basic

Question 2: Which statement is true for a typical machine learning project in Watson Studio?

• Comes with everything included
• Needs an associated Cloud Object Storage only
• Needs an associated Machine Learning service only
• Needs an associated Spark service only
• Needs Cloud Object Storage, a Machine Learning service, and a Spark service

Question 3: What’s the best way to select columns in a machine learning model in Watson Studio?

• Always select all columns as features
• Select a prediction column and all remaining columns as features
• Select a prediction column and select which remaining, sometimes all, columns are features
• Select only a prediction column

Question 4: What does a 60%, 20%, 20% split of data mean?

• 60% of the data is used to train models, 20% to test the models, and 20% to test for overfitting.
• Checks that more that 60% of the predictions are correct, no more than 20% are incorrect, and no more than 20% are borderline.
• Takes no more than 60 steps to prepare an estimate and no more than 20 steps to determine the certainty.
• 20% of the data is used to train the models, 60% of the data is used to test the models, and 20% to test for overfitting

Question 5: How does an overfitting model perform?

• Performs well on all data
• Performs well on data that is used to select the model but performs poorly with other data
• Performs badly on all data
• Performs poorly on data that is used to select the model but performs well with other data

Lab 3: Create Predictions in a Node-RED Application

Question 1: Node-RED allows you to import and export flows.

• True
• False

Question 2: In Node-RED, you can install nodes by using which method?

• Deploy option
• The Dashboard
• Manage Palette option
• The Settings option

Question 3: Which combination of Node-RED nodes are required to inject audio into a flow?

• file inject and microphone
• inject and microphone
• file inject and inject
• file inject, inject, and microphone

Question 4: You can use the http request node to do which task?

• Encode a data buffer
• Invoke a REST API
• Create a HTTP endpoint
• Deliver an API response

Question 5: A machine learning classification prediction response contains which items?

• Raw prediction for each class, probability for each class, all classes
• Raw prediction for each class, probability for each class,
• Raw prediction for each class, probability for each class, prediction, prediction class
• Raw prediction for each class, probability for each class, prediction, prediction class, all classes

Lab 4: Create Multiclass Classification Models

Question 1: When do you use a multiclass classification?

• When your label column contains two distinct categories
• When your label column contains a discrete number of categories
• When your label column contains a large number of values
• When your label column contains different data types

Question 2: You run machine learning predictions against which type of model or data?

• Deployed models
• Undeployed models
• Generated models
• Selected estimators

Question 3: What does each Lite Plan instance of the Watson Machine Learning service allow?

• Multiple deployments of only 1 model
• Single deployments of any number of models
• A maximum total of 5 deployments of any number of models
• Any number of deployments of a maximum of 5 models

Question 4: What is an application prediction?

• The prediction from one deployed model
• An application consolidated prediction from any number of deployed models
• The best prediction from up to five deployed models
• The best and worst prediction from up to five deployed models

Question 5: An application must consider only the highest probability scoring prediction from a prediction.

• True
• False

Lab 5: Create UIs and Integrate Visual Recognition

Question 1: The following HTML code in the Node-RED UI application allows the HTML web page to process JavaScript.

    <script>{{{payload.script}}}</script>

• True
• False

Question 2: One way to train the Watson Visual Recognition service is to feed it positive images of what you want to predict, say, domestic cats, and negative images, say, dogs, lions, birds, and other animals, that you don’t want to predict.

• True
• False

Question 3: Which type of node is a one-way communication link that can update a web page every time the Machine Learning service makes a prediction?

• http input
• websocket
• machine learning
• prediction

Question 4: If the Node-RED application for Lab 5 processes nine machine learning models, how many machine learning nodes are required?

• 1
• 3
• 6
• 9

Question 5: The Visual Recognition service is simply an API that you can connect to by using http input and output nodes.

• True
• False

Introduction to Machine Learning with Sound Final Exam – Cognitive Class

Question 1: If you split your data by 70%, 20%, 10%, which percentage is used for the training data?

• 100%
• 70%
• 20%
• 10%

Question 2: You can use digital signal processing to create numbers for audio files so that you can compare the audio files and then use these numbers as the basis of a machine learning model.

• True
• False

Question 3: In which Node-RED node do you set the Mode field to run a prediction?

• Build Payload Values function node
• Hardcoded test node
• Watson Machine Learning (WML) node
• Prediction Columns node

Question 4: When you create new projects in Watson Studio, a machine learning service is automatically associated with the new project.

• True
• False

Question 5: The Naïve Bayes estimator does not work with data that contains negative numbers.

• True
• False

Question 6: In this course, you use Cloud Object Storage to store data files, such as CSV files.

• True
• False

Question 7: In Lab 3 of this course, you ran a hardcoded prediction test by using Build Payload Values function node. In the code for this function node, why do the columns start with column 2 rather than column 1?

• Because column 2 contains the value that the machine learning model will be predicting
• Because column 1 includes all the data
• Because column 1 is a feature column
• Because column 1, which is not a feature column, is the value that the machine learning model will be predicting

Question 8: Why is it necessary to deploy the Python Flask digital signal processing application in Lab 3?

• So that you can feed an audio file to the machine learning node in the Node-RED application
• So that you can run predictions in IBM Cloud
• So that your final application for this course can be provided as an API
• So that you can refetch model lists that include sound files

Question 9: After you deploy a model in Watson Studio, you see a deployment ID. You use this ID to call the predictor in Node-RED or other application.

• True
• False

Question 10: The Watson Visual Recognition service can be trained to recognize both audio and images.

• True
• False

Introduction to Machine Learning with Sound

Machine learning (ML) with sound involves using algorithms to analyze, process, and extract meaningful information from audio data. This field has gained significant attention in recent years due to its wide range of applications, including speech recognition, music generation, environmental monitoring, healthcare diagnostics, and more.

Here’s a basic overview of how machine learning is applied to sound:

1. Data Collection: The first step in any machine learning project is gathering data. In the case of sound, this involves recording audio samples using microphones or accessing existing audio datasets.
2. Feature Extraction: Once the audio data is collected, relevant features need to be extracted to represent the characteristics of the sound. These features could include frequency, amplitude, duration, and spectral content. Feature extraction is crucial as it simplifies the data and makes it more amenable to analysis by machine learning algorithms.
3. Model Training: After feature extraction, machine learning models are trained using labeled audio data. There are various types of machine learning algorithms that can be used, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms learn to make predictions based on labeled examples, while unsupervised learning algorithms find patterns and structures in unlabeled data. Reinforcement learning involves learning to make decisions by interacting with an environment.
4. Model Evaluation and Testing: Once the models are trained, they need to be evaluated using validation data to assess their performance and ensure they generalize well to unseen data. This step is crucial for identifying and mitigating issues such as overfitting.
5. Deployment: After successful evaluation, the trained model can be deployed to perform tasks such as speech recognition, sound classification, or anomaly detection in real-world applications.

Some common applications of machine learning with sound include:

• Speech Recognition: Transcribing spoken words into text.
• Speaker Identification: Recognizing who is speaking based on their voice.
• Music Genre Classification: Categorizing music into different genres.
• Environmental Sound Recognition: Identifying sounds in the environment, such as alarms, sirens, or animal calls.
• Healthcare Diagnostics: Analyzing medical data such as heartbeat sounds for diagnostic purposes.

In summary, machine learning with sound opens up a wide range of possibilities for analyzing and understanding audio data, with applications across various industries and domains.