Home AI Courses ML: Clustering & Retrieval

ML: Clustering & Retrieval

(0 reviews)

Using k-nearest neighbors, k-means, and probabilistic modeling techniques, participants will use Python to develop clustering and document retrieval systems.

Key AI Functions:data clustering algorithms,k-means clustering,machine learning,k-d tree,ai & machine learning

Description

Reviews

Alternative Tools

Description for ML: Clustering & Retrieval

Methods of Document Retrieval: Develop a document retrieval system that employs k-nearest neighbors and employs methods to reduce computational burden, such as KD-trees and locality-sensitive hashing.
Approaches to Clustering: Utilize k-means to cluster documents by topic, and employ MapReduce to parallelize the process. Additionally, investigate probabilistic clustering with mixture models.
Proficient Probabilistic Modeling: Fit Gaussian mixture models through expectation maximization (EM) with Gibbs sampling, and perform mixed membership modeling using latent Dirichlet allocation (LDA).
Optimization and Implementation: Compare the initialization methods for non-convex optimization objectives and implement these clustering and modeling techniques in Python.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by University of Washington

Duration: 3 weeks at 5 hours a week

Schedule: Flexible

Reviews for ML: Clustering & Retrieval

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Gen AI: Foundation Models and Platforms

Mastering Gen AI: Advanced Fine-Tuning for LLMs

Alternative Tools for ML: Clustering & Retrieval

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.

#research #marketing

Gen AI Assisting Data Scientists

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.

#Artificial Intelligence (AI) #Data Science

GenAI An Introductory Course

Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.

#Artificial Intelligence (AI) #Python Programming

ML on Google Cloud

Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world

#Tensorflow #Machine Learning

Gen AI in Marketing Specialization

Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.

#Generative AI #Large Language Models

Gen AI - Art of the Possible An Intro

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.

#Generative AI #Amazon Web Services

Retrieval Augmented Generation (RAG) - Intro

Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.

#Python Programming #Machine Learning

Vertex AI - An Introduction

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.

#Critical Thinking #MLOps (Machine Learning Operations)

AI and ML Essentials with Python Specialization

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.

#Artificial Intelligence #Python (Programming Language)

Machine Learning with Python

Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.

#Machine Learning #regression