What does a larger dataset actually mean?
A larger dataset in the realm of artificial intelligence, particularly within Google Cloud Machine Learning, refers to a collection of data that is extensive in size and complexity. The significance of a larger dataset lies in its ability to enhance the performance and accuracy of machine learning models. When a dataset is large, it contains
What are some examples of algorithm’s hyperparameters?
In the realm of machine learning, hyperparameters play a crucial role in determining the performance and behavior of an algorithm. Hyperparameters are parameters that are set before the learning process begins. They are not learned during training; instead, they control the learning process itself. In contrast, model parameters are learned during training, such as weights
What if a chosen machine learning algorithm is not suitable and how can one make sure to select the right one?
In the realm of Artificial Intelligence (AI) and machine learning, the selection of an appropriate algorithm is crucial for the success of any project. When the chosen algorithm is not suitable for a particular task, it can lead to suboptimal results, increased computational costs, and inefficient use of resources. Therefore, it is essential to have
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Does Google Vision API enable facial recognition?
The Google Cloud Vision API is a powerful tool that provides various image analysis capabilities, including the detection and recognition of faces within images. However, it is essential to clarify the distinction between facial detection and facial recognition to address the question at hand. Facial detection, also known as face detection, is the process of
How does one implement an AI model that does machine learning?
To implement an AI model that performs machine learning tasks, one must understand the fundamental concepts and processes involved in the machine learning. Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. Google Cloud Machine Learning provides a platform and tools
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How does one know when to use supervised versus unsupervised training?
Supervised and unsupervised learning are two fundamental types of machine learning paradigms that serve distinct purposes based on the nature of the data and the objectives of the task at hand. Understanding when to use supervised training versus unsupervised training is crucial in designing effective machine learning models. The choice between these two approaches depends
How does one know if a model is properly trained? Is accuracy a key indicator and does it have to be above 90%?
Determining whether a machine learning model is properly trained is a critical aspect of the model development process. While accuracy is an important metric (or even a key metric) in evaluating the performance of a model, it is not the sole indicator of a well-trained model. Achieving an accuracy above 90% is not a universal
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is machine learning?
Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It is a powerful tool that allows machines to automatically analyze and interpret complex data, identify patterns, and make informed decisions or predictions.
Can machine learning predict or determine the quality of the data used?
Machine Learning, a subfield of Artificial Intelligence, has the capability to predict or determine the quality of the data used. This is achieved through various techniques and algorithms that enable machines to learn from the data and make informed predictions or assessments. In the context of Google Cloud Machine Learning, these techniques are applied to
How can you programmatically extract labels from images using Python and the Vision API?
To programmatically extract labels from images using Python and the Vision API, you can leverage the powerful capabilities of the Google Cloud Vision API. The Vision API provides a comprehensive set of image analysis features, including label detection, which allows you to automatically identify and extract labels from images. To get started, you will need
- Published in Artificial Intelligence, EITC/AI/GVAPI Google Vision API, Labelling images, Labels detection, Examination review