Home AI Courses Foundations of Machine Learning

Foundations of Machine Learning

(0 reviews)

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

Key AI Functions:Logistic Regression,Unsupervised Learning,Data Pre-Processing,Linear Regression,Decision Tree

Description

Reviews

Alternative Tools

Description for Foundations of Machine Learning

Utilize the varied stages of a typical machine learning workflow to develop machine learning models.

Evaluate the efficacy of machine learning models by employing suitable metrics for a variety of business challenges.

Create machine learning models that are based on regression and trees to make predictions about pertinent business issues.

Examine business issues that could be resolved by employing unsupervised machine learning models to extract value from data.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by Fractal Analytics

Duration: 25 hours (approximately)

Schedule: Flexible

Reviews for Foundations of Machine Learning

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

TensorFlow 2: Deep Learning Specialization

CertNexus Certified AI Practitioner Professional Certificate

Foundations of Data Science: K-Means Clustering in Python

Using Machine Learning in Trading and Finance

QnA Chatbot Development: From Concept to Deployment

Alternative Tools for Foundations of Machine Learning

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)

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

Mathematics for ML & Data Science Specialization

Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.

#Bayesian Statistics #Mathematics

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

Machine Learning Basics

Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.

#Machine Learning #Python

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