ML Regression

(0 reviews)

Master regression by predicting house prices, investigate regularized linear regression, manage extensive feature sets, and employ optimization algorithms to make precise predictions with large datasets.

Key AI Functions:Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis

Description

Reviews

Alternative Tools

Description for ML Regression

Case Study on House Price Prediction: Using input features such as square footage and the number of bedrooms, create models to predict house prices, illustrating the effectiveness of regression.

Regularized Linear Regression: Investigate the application of regularized linear regression models in a variety of domains for the purpose of feature selection and prediction.

Managing Large Feature Sets: Develop the ability to manage extensive sets of features and select between models of varying complexities, while also evaluating the impact of outliers on predictions.

Optimization Algorithms: Employ optimization algorithms to develop models that can accommodate large datasets in order to make precise predictions.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by University of Washington

Duration: 22 hours (approximately)

Schedule: Flexible

Reviews for ML Regression

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Simple Games with AI

AI Tailored to Kids

AI Chatbots without Programming

Models and Platforms for Gen AI

AI Prompt Engineering for Beginners

Alternative Tools for ML Regression

CertNexus Certified AI Practitioner Professional Certificate

Become an expert in the field of artificial intelligence. Develop effective strategies for the application of Artificial Intelligence techniques to address business challenges.

#Linear Regression #Machine Learning (ML) Algorithms

ML in Mathematics: Multivariate Calculus

This course offers a concise summary of essential multivariate calculus for machine learning, including practical tools, vector calculations, function approximation, and neural network applications, to build confidence for advanced studies.

#Linear Regression #Vector Calculus

Data Science Specialization

Commence Your Career in Data Science. A ten-course introduction to data science, devised and instructed by distinguished professors.

#Github #Machine Learning

Microsoft Azure ML for Data Scientists

Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.

#Microsoft Azure #Machine Learning

Supervised ML: Regression

Besides Python programming and data science fundamentals, the course covers supervised machine learning regression, which includes training models for continuous outcomes, error metrics, Elastic Net, LASSO, Ridge regularization, and data science fundamentals for aspiring data scientists.

#Linear Regression #Machine Learning (ML) Algorithms

Supervised ML: Regression and Classification

Learn to build and train supervised machine learning models for binary classification and prediction tasks using Python with NumPy and scikit-learn libraries.

#Linear Regression #Regularization to Avoid Overfitting