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
In order to balance or improve the integration of AI in education, this course examines conversational AI technologies and provides evaluation designs.
Achieve a professional status as an AI Engineer. Acquire the knowledge necessary to develop next-generation applications that are propelled by generative AI, a skill that is indispensable for startups, agencies, and large corporations.
This AI course instructs data scientists on the development of automated algorithms using Watson Studio's AutoAI, with an emphasis on hyperparameter optimization, feature engineering, and model selection.
Understand Python methodologies like lambdas, csv file manipulation, and prevalent data science features, including cleansing and processing DataFrame structures.
Exploration of ethical dilemmas in Fraud Detection and email spam classification models, alongside Generative AI collaboration.