Create an artificial neural network that would host the company directory.Which machine learning project would add the most value for the company! The algorithms can run on unstructured data.Machine learning servers can host larger databases.The algorithms are better at seeing patterns in the data.The algorithms are typically run more powerful servers.How do machine learning algorithms make more precise predictions? Machine learning and artificial intelligence are the same thing.Artificial intelligence is form of unsupervised machine learning.Machine learning is a type of artificial intelligence that relies on learning through data.Artificial intelligence focuses on classification, while machine learning is about clustering data.How is machine learning related to artificial intelligence? What is the best description of this chart? You work for an ice cream shop and created the chart below, which shows the relationship between the outside temperature and ice cream sales. It naively assumes that all the predictors depend on one another.It naively assumes that the predictors are independent from one another.It does not even try to create accurate predictions.It naively assumes that you will have no data.By using machine learning algorithms, you are creating an IoT device.The algorithms would help your organization see patterns of the data.The algorithms will improve the wireless connectivity.The algorithms would help the meters access the internet.Why are machine learning algorithms ideal in this scenario? Your supervisor asks you to direct project to use machine learning to analyze this usage data.

These meters are connected to the internet and transmit energy usage data in real-time. You work for a power company that owns hundreds of thousands of electric meters. What is an example of a commercial application for a machine learning system? _ looks at the relationship between predictors and your outcome. You work in a data science team that wants to improve the accuracy of its K-nearest neighbor result by running on top of a naive Bayes result.

Create a training set of normal weather and have the machine look for similar patterns.Create a training set of unusual patterns and ask the machine learning algorithms to classify them.Use unsupervised learning have the machine look for anomalies in a massive weather database.Find labeled data of sunny days so that the machine will learn to identify bad weather.To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. You want to identify global weather patterns that may have been affected by climate change. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. In traditional computer programming, you input commands. Now you pull out a small random subset of all the songs in your service. Your service has collected thousands of songs in each genre, and you used this as your training data. You work for a music streaming service and want to use supervised machine learning to classify music into different genres. The product could find spam messages using far fewer keywords.The product could have a much longer keyword list.The product could go through the keyword list much more quickly.The product would look for new patterns in spam messages.What would be one advantage of transitioning to machine learning? If a message contains more than few of these keywords, then it is identified as spam. Your organization wants to transition its product to use machine learning. You work for an organization that sells a spam filtering service to large companies. What is the most likely diagram that your team created? You create a simple report that shows trend: Customers who visit the store more often and buy smaller meals spend more than customers who visit less frequently and buy larger meals. You are part of a data science team that is working for a national fast-food chain.

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