Can Machine Learning adapt depending on a scenario outcome which alforithm to use?
Machine learning (ML) is a discipline within artificial intelligence that focuses on building systems capable of learning from data and improving their performance over time without being explicitly programmed for each task. A central aspect of machine learning is algorithm selection: choosing which learning algorithm to use for a particular problem or scenario. This selection
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How does an already trained machine learning model takes new scope of data into account?
When a machine learning model is already trained and encounters new data, the process of integrating this new scope of data can take several forms, depending on the specific requirements and context of the application. The primary methods to incorporate new data into a pre-trained model include retraining, fine-tuning, and incremental learning. Each of these
How to limit bias and discrimination in machine learning models?
To effectively limit bias and discrimination in machine learning models, it is essential to adopt a multi-faceted approach that encompasses the entire machine learning lifecycle, from data collection to model deployment and monitoring. Bias in machine learning can arise from various sources, including biased data, model assumptions, and the algorithms themselves. Addressing these biases requires
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How to protect the privacy of data used to train machine learning models?
Protecting the privacy of data used to train machine learning models is a critical aspect of responsible AI development. It involves a combination of techniques and practices designed to ensure that sensitive information is not exposed or misused. This task has become increasingly important as the scale and complexity of machine learning models grow, and
How to ensure transparency and understandability of decisions made by machine learning models?
Ensuring transparency and understandability in machine learning models is a multifaceted challenge that involves both technical and ethical considerations. As machine learning models are increasingly deployed in critical areas such as healthcare, finance, and law enforcement, the need for clarity in their decision-making processes becomes paramount. This requirement for transparency is driven by the necessity
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Where is the information about a neural network model stored (including parameters and hyperparameters)?
In the domain of artificial intelligence, particularly concerning neural networks, understanding where information is stored is important for both model development and deployment. A neural network model consists of several components, each of which plays a distinct role in its operation and efficacy. Two of the most significant elements within this framework are the model's
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the difference between machine learning in computer vision and machine learning in LLM?
Machine learning, a subset of artificial intelligence, has been applied to various domains, including computer vision and language learning models (LLMs). Each of these fields leverages machine learning techniques to solve domain-specific problems, but they differ significantly in terms of data types, model architectures, and applications. Understanding these differences is essential to appreciate the unique
How essential is Python or other programming language knowledge to implement ML in practice?
To address the question of how necessary Python or any other programming language knowledge is for implementing machine learning (ML) in practice, it is vital to understand the role programming plays in the broader context of machine learning and artificial intelligence (AI). Machine learning, a subset of AI, involves the development of algorithms that allow
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Why is the step of evaluating a machine learning model’s performance on a separate test dataset essential, and what might happen if this step is skipped?
In the field of machine learning, evaluating a model's performance on a separate test dataset is a fundamental practice that underpins the reliability and generalizability of predictive models. This step is integral to the model development process for several reasons, each contributing to the robustness and trustworthiness of the model's predictions. Firstly, the primary purpose
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the true value of machine learning in today’s world, and how can we distinguish its genuine impact from mere technological hype?
Machine learning (ML), a subset of artificial intelligence (AI), has become a transformative force in various sectors, offering substantial value by enhancing decision-making processes, optimizing operations, and creating innovative solutions to complex problems. Its true value lies in its ability to analyze vast amounts of data, identify patterns, and generate predictions or decisions with minimal
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning