Foundations of Data Science: K-Means Clustering in Python
Leverage Python programming skills to develop and analyze comprehensive clustering procedures, thereby mastering the fundamental concepts and operations of data clustering, with a particular emphasis on the K-means algorithm.
Description for Foundations of Data Science: K-Means Clustering in Python
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Goldsmiths, University of London
Duration: 3 weeks at 9 hours a week
Schedule: Flexible
Pricing for Foundations of Data Science: K-Means Clustering in Python
Use Cases for Foundations of Data Science: K-Means Clustering in Python
FAQs for Foundations of Data Science: K-Means Clustering in Python
Reviews for Foundations of Data Science: K-Means Clustering in Python
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Foundations of Data Science: K-Means Clustering in Python
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.
Investigate the field of artificial intelligence and machine learning. While investigating the transformative disciplines of artificial intelligence, machine learning, and deep learning, enhance your Python abilities.
Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.
Featured Tools
Acquire proficiency in the development of sophisticated LLM applications, the utilization of AI coding assistants, and the effective management of professional deployment techniques in the AI sector.
While addressing real-world issues and utilizing scientific datasets, develop a comprehensive understanding of machine learning techniques and tools.
Empowers learners with practical knowledge of AI strategies and tools to promote innovation and efficacy in business and beyond.
A data analysis course covering practical skills, data visualization in Excel and BI tools, Python for data analysis, and portfolio development through hands-on projects.
Obtain practical experience in the development, testing, and deployment of a variety of AI/ML models by utilizing advanced techniques such as ResNets and transfer learning, as well as no-code tools.