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

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

Operationalizing LLMs on Azure

ML in Sports Analytics An Introduction

Automated Reasoning with GPT Assistant API: ReAct Agents

Mastering Gen AI: Model Foundations and NLP

Modern Robotics: Mechanics, Planning, and Control Specialization

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