Home AI Courses Practical Machine Learning

Practical Machine Learning

(0 reviews)

Learn to construct and implement prediction functions, understand overfitting and error rates, and grasp machine learning techniques like classification trees and regression.

Key AI Functions:Random Forest,Machine Learning (ML) Algorithms,Machine Learning,R Programming

Description

Reviews

Alternative Tools

Description for Practical Machine Learning

Utilize the fundamental components of constructing and implementing prediction functions.

Comprehend the concepts of overfitting, error rates, and training and test sets.

Define machine learning techniques, including classification trees and regression.

Describe the entire process of developing prediction functions.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Johns Hopkins University

Duration: 8 hours (approximately)

Schedule: Flexible

Reviews for Practical Machine Learning

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

AI and ML on GC an Intro - Espanol

Machine Learning in Trading Specialization

ML con Spark (MLlib) en Databricks

Machine Learning Foundations

Supervised ML: Classification

Alternative Tools for Practical Machine Learning

Featured Tools

AI and ML on GC an Intro - Espanol

Learn how to develop AI and ML on Google Cloud with tools that are specifically designed to facilitate seamless integration throughout the data-to-AI lifecycle.

#artificial intelligence #machine learning

Machine Learning in Trading Specialization

Begin Your Career in Trading with Machine Learning. Familiarize yourself with the machine learning methodologies employed in quantitative trading.

#Learning Model Development #Algorithm Optimization

ML con Spark (MLlib) en Databricks

Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.

#Machine Learning #MLlib

Machine Learning Foundations

Work on the importance of two companion courses in machine learning, covering both mathematical and algorithmic tools, essential for practitioners to master the field.

#Decision Stump #Perceptron

Supervised ML: Classification

This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.

#Ensemble Learning #Machine Learning (ML) Algorithms

Compare & Review AI Tool Directory AI Course Marketplace