Home AI Courses ML Algorithms: Supervised Learning Tip to Tail

ML Algorithms: Supervised Learning Tip to Tail

(0 reviews)

This course concentrates on the fundamentals of machine learning, including decision trees, k-nearest neighbors, and support vector machines. It addresses data preparation and production challenges and requires a rudimentary understanding of Python, linear algebra, and statistics.

Key AI Functions:

Machine Learning,Supervised Learning,Decision Tree

Description

Reviews

Alternative Tools

Description for ML Algorithms: Supervised Learning Tip to Tail

The course delves into the fundamentals of a machine learning project, with a particular emphasis on support vector machines, decision trees, and k-nearest neighbors for the purpose of conducting real-world case studies.

It instructs on the comparison of data preparation procedures and the resolution of production challenges in the context of applied machine learning.

A fundamental comprehension of linear algebra and statistics, as well as a basic understanding of Python programming, are necessary.

This is the second course in the Applied Machine Learning Specialization, which is offered by the Alberta Machine Intelligence Institute and Coursera.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Alberta Machine Intelligence Institute

Duration: 9 hours (approximately)

Schedule: Flexible

Reviews for ML Algorithms: Supervised Learning Tip to Tail

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Gen AI Guiding Product Managers

AI in Business

Gen AI Assistants

Gen AI GPT Vision

Gen AI Leadership & Strategy

Alternative Tools for ML Algorithms: Supervised Learning Tip to Tail

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

Machine Learning with Python

Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.

#Machine Learning #regression

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

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

Advanced ML & Signal Processing

Gain foundational knowledge of Linear Algebra and Machine Learning models, explore the scalability of SparkML and Scikit-Learn, and gain practical experience by adjusting models and analyzing vibration sensor data in a real-world IoT example.

#Machine Learning #Signal Processing

ML with Apache Spark

Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.

#Machine Learning #Machine Learning Pipelines

ML Classification

Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.

#Logistic Regression #Statistical Classification