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
Is testing a ML model against data that could have been previously used in model training a proper evaluation phase in machine learning?
The evaluation phase in machine learning is a critical step that involves testing the model against data to assess its performance and effectiveness. When evaluating a model, it is generally recommended to use data that has not been seen by the model during the training phase. This helps to ensure unbiased and reliable evaluation results.
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Is inference a part of the model training rather than prediction?
In the field of machine learning, specifically in the context of Google Cloud Machine Learning, the statement "Inference is a part of the model training rather than prediction" is not entirely accurate. Inference and prediction are distinct stages in the machine learning pipeline, each serving a different purpose and occurring at different points in the
Which ML algorithm is suitable to train model for data document comparison?
One algorithm that is well suited to train a model for data document comparison is the cosine similarity algorithm. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. In the context of document comparison, it is used to determine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What are the main differences in loading and training the Iris dataset between Tensorflow 1 and Tensorflow 2 versions?
The original code provided to load and train the iris dataset was designed for TensorFlow 1 and may not work with TensorFlow 2. This discrepancy arises due to certain changes and updates introduced in this newer version of TensorFlow, which wll be however covered in detail in subsequent topics that will directly relate to TensorFlow
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators
Machine learning algorithms can learn to predict or classify new, unseen data. What does the design of predictive models of unlabeled data involve?
The design of predictive models for unlabeled data in machine learning involves several key steps and considerations. Unlabeled data refers to data that does not have predefined target labels or categories. The goal is to develop models that can accurately predict or classify new, unseen data based on patterns and relationships learned from the available
How to build a model in Google Cloud Machine Learning?
To build a model in the Google Cloud Machine Learning Engine, you need to follow a structured workflow that involves various components. These components include preparing your data, defining your model, and training it. Let's explore each step in more detail. 1. Preparing the Data: Before creating a model, it is crucial to prepare your
Why the evaluation is 80% for training and 20% for evaluating but not the opposite?
The allocation of 80% weightage to training and 20% weightage to evaluating in the context of machine learning is a strategic decision based on several factors. This distribution aims to strike a balance between optimizing the learning process and ensuring accurate evaluation of the model's performance. In this response, we will delve into the reasons
What are weights and biases in AI?
Weights and biases are fundamental concepts in the field of artificial intelligence, specifically in the domain of machine learning. They play a crucial role in the training and functioning of machine learning models. Below is a comprehensive explanation of weights and biases, exploring their significance and how they are used in the context of machine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the definition of a model in machine learning?
A model in machine learning refers to a mathematical representation or algorithm that is trained on a dataset to make predictions or decisions without being explicitly programmed. It is a fundamental concept in the field of artificial intelligence and plays a crucial role in various applications, ranging from image recognition to natural language processing. In
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning