Home AI Courses Supervised ML: Classification

Supervised ML: Classification

(0 reviews)

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

Key AI Functions:Ensemble Learning,Machine Learning (ML) Algorithms,Supervised Learning,Classification Algorithms,Decision Tree

Description

Reviews

Alternative Tools

Description for Supervised ML: Classification

Features of Course

Learn to train predictive models for the classification of categorical outcomes and compare models using error metrics, with a particular emphasis on classification in supervised machine learning.

Acquire practical experience in the application of classification best practices, including the management of unbalanced datasets and the implementation of train-test divisions.

Become proficient in a variety of techniques, such as alternative ensemble algorithms, tree-ensemble models, decision trees, and logistic regression, as well as error metrics for model comparison.

This course is designed for individuals who are interested in becoming data scientists and possess a fundamental understanding of Python programming, calculus, linear algebra, probability, statistics, and exploratory data analysis.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by IBM

Duration: 24 hours (approximately)

Schedule: Flexible

Reviews for Supervised ML: Classification

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

AI, Business & the Future of Work

ML & Human Learning

Planning Gen AI Project

Gen AI in Education

OpenAI GPTs: Custom AI Assistants

Alternative Tools for Supervised ML: Classification

Foundations of Machine Learning

Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.

#Logistic Regression #Unsupervised Learning

ML Specialization

Specialization in Machine Learning at BreakIntoAI. Master the fundamental AI concepts and cultivate practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.

#Logistic Regression #Artificial Neural Network

Machine Learning Specialization

Develop applications that are intelligent. In four practical courses, acquire a comprehensive understanding of the fundamentals of machine learning.

#Data Clustering Algorithms #Machine Learning

IBM Machine Learning Professional Certificate

Set up for a profession in machine learning. To become job-ready in less than three months, acquire the skills and practical experience that are in high demand.

#Statistical Hypothesis Testing #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

IBM Intro to ML Specialization

Acquire knowledge of machine learning by examining actual applications. Develop the necessary skills for a vocation in one of the most pertinent areas of contemporary AI by participating in hands-on projects and completing coursework from IBM's experts.

#Unsupervised Learning #Machine Learning

ML : Algorithms in the Real World Specialization

Real-World Applications of Machine Learning. Develop proficiency in the implementation of a machine learning undertaking.

#Project Management #Machine Learning (ML) Algorithms

ML Theory & Hands-on: Python Specialization

Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.

#Unsupervised Learning #Python Programming

Probability & Statistics for Machine Learning & Data Science

Understand and apply statistical techniques to quantify prediction uncertainty, analyze probability distributions, and evaluate machine learning model efficacy using interval estimates and margins of error.

#Probability And Statistics #Machine Learning (ML) Algorithms

Practical Machine Learning

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

#Random Forest #Machine Learning (ML) Algorithms