Which three statements about machine learning are true? (Choose three.)
A) Machine learning will eventually develop to a level where it can replace advertisers entirely.
B) Machine learning is a way of effectively summarizing large amounts of data.
C) Machine learning happens when computers analyze and recognize patterns in huge amounts of data.
D) Machine learning means computers don’t need to be explicitly programmed or told what to do.
E) Machine learning can become more efficient and accurate over time.
Correct Answer is: C, D, and E
Explanation:
Machine learning is a field of computer science that focuses on the development of algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed. In this article, we will discuss three statements about machine learning that are true.
Statement 1: Machine learning models can be trained on both structured and unstructured data
One of the key advantages of machine learning is its ability to handle both structured and unstructured data. Structured data refers to data that is organized in a predefined format, such as a database or a spreadsheet, and can be easily analyzed using mathematical or statistical techniques. Unstructured data, on the other hand, refers to data that is not organized in a predefined format, such as text, images, or videos, and requires more advanced techniques to be analyzed.
Machine learning models can be trained on both structured and unstructured data using a variety of techniques, such as regression, classification, clustering, and deep learning. For structured data, machine learning models can use techniques such as linear regression, decision trees, and random forests to analyze and make predictions based on the data. For unstructured data, machine learning models can use techniques such as natural language processing, computer vision, and convolutional neural networks to analyze and classify the data.
Statement 2: Machine learning models require large amounts of data to achieve high accuracy
Machine learning models require large amounts of data to achieve high accuracy because they learn by finding patterns in the data. The more data that is available for the model to learn from, the more accurate the model is likely to be. This is because the model has a greater chance of finding patterns that are relevant to the problem being solved.
The amount of data required to train a machine learning model depends on the complexity of the problem being solved and the complexity of the model itself. For simple problems and models, a few hundred or thousand data points may be sufficient. However, for more complex problems and models, millions or even billions of data points may be required.
Statement 3: Machine learning models can be prone to bias
Machine learning models can be prone to bias because they learn from historical data, which may contain biases and inaccuracies. If the historical data contains biases, these biases can be learned and reinforced by the model, leading to biased predictions or decisions.
There are several types of bias that can occur in machine learning models, such as selection bias, measurement bias, and algorithmic bias. Selection bias occurs when the data used to train the model is not representative of the population being analyzed. Measurement bias occurs when the data used to train the model is inaccurate or incomplete. Algorithmic bias occurs when the algorithm used to train the model has inherent biases or assumptions.
To address bias in machine learning models, it is important to carefully select and preprocess the data used to train the model, and to evaluate the model’s performance on diverse and representative datasets. Additionally, it may be necessary to incorporate ethical considerations and human oversight into the development and deployment of machine learning models.
Conclusion
Machine learning is a powerful tool for analyzing and making predictions based on data. Machine learning models can be trained on both structured and unstructured data, require large amounts of data to achieve high accuracy, and can be prone to bias. Understanding these three statements about machine learning is crucial for developing effective machine learning models and ensuring their accuracy, fairness, and reliability.