Pickl AI

JANUARY 12, 2025

Moreover, regulatory requirements concerning data utilisation, like the EU’s General Data Protection Regulation GDPR, further complicate the situation. Such challenges can be mitigated by durable data governance, continuous training, and high commitment toward ethical standards.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Pickl AI

OCTOBER 18, 2023

Data Enrichment Services Enrichment tools augment existing data with additional information, such as demographics, geolocation, or social media profiles. This enhances the depth and usefulness of the data. It defines roles, responsibilities, and processes for data management.

Data Quality 52

Data Quality ML ML Machine Learning 52

DagsHub

SEPTEMBER 23, 2024

Ethical Concerns Conventional machine learning models, such as linear regressions and decision trees, often operate within well-defined domains. Although their outputs are usually derived from structured data with easily recognizable patterns, they can still present ethical challenges.

Decision Trees 52

Decision Trees Data Governance Artificial Intelligence Artificial Intelligence 52

Pickl AI

APRIL 26, 2024

What are the advantages and disadvantages of decision trees ? Advantages: It is easy to interpret and visualise, can handle numerical and categorical data, and requires fewer data preprocessing. Data Governance and Ethics Questions What is data governance, and why is it important?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

AUGUST 9, 2024

Key topics include: Supervised Learning Understanding algorithms such as linear regression, decision trees, and support vector machines, and their applications in Big Data. Students should learn about data validation techniques and the importance of data governance.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Defined.ai blog

AUGUST 10, 2023

Meanwhile, ML is the mechanism that enables the AI to learn from the data, improve over time, and make more accurate predictions. For instance, regression algorithms in Machine Learning are widely employed to predict stock prices based on historical data. Data Quality For AI to produce reliable results, it needs high-quality data.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analysis Data Analysis 52

Heartbeat

MAY 29, 2023

The weak models can be trained using techniques such as decision trees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. Algorithmic Transparency: Developing LLMs that are transparent and explainable, enabling stakeholders to understand the decisions made by the models.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78