ML Classification

(0 reviews)

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

Key AI Functions:Logistic Regression, Statistical Classification, Classification Algorithms, Decision Tree

Description

Reviews

Alternative Tools

Description for ML Classification

Case Studies: Engage in sentiment analysis and loan default prediction, representing classification tasks with broad applications like image classification and spam detection.

Techniques: Learn cutting-edge classification techniques such as logistic regression, decision trees, and boosting, along with employing stochastic gradient ascent for large-scale learning.

Challenges: Tackle real-world ML challenges like evaluating classifiers, handling missing data, and understanding precision-recall metrics.

Learning Objectives: Master classification model input-output, logistic regression, decision trees, boosting, and precision-recall metrics in Python, enabling effective classification on diverse datasets.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by University of Washington

Duration: 21 hours (approximately)

Schedule: Flexible

Reviews for ML Classification

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Higher education assessing in the age of AI

ML with Python: A Practical Introduction

Teaching and Learning in the Era of AI

ML Guide: Learn ML Algorithms

Predict the stock market with data and model building!

Alternative Tools for ML Classification

Machine Learning Essentials

This course teaches Python-based machine learning techniques, including linear regression and classification.

#logistic regression #linear regression

Classification Of Machine Learning In Kyphosis Desease

Utilize Sklearn to create decision tree and random forest models for the prediction of kyphosis, with potential applications in healthcare diagnostics.

#data science #python programming

Applied ML An Introduction

It provides professionals with the necessary skills to define machine learning problems, prepare data, and identify applications across a variety of domains.

#project management #machine learning (ml) algorithms

ML with Spark on Google Cloud Dataproc

The purpose of this course is to provide students with the opportunity to develop practical, cloud-based machine learning skills. It focuses on the use of Apache Spark to teach logistic regression modeling on Google Cloud.

#logistic regression #google cloud platform

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

ML: Concepts & Applications

Learn to use Python and libraries for data tasks, understand key machine learning techniques, and apply them to real-world datasets for a strong research foundation.

#Unsupervised Learning #Artificial Neural Network

Detecting COVID-19 with Chest X-Ray using PyTorch

Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.

#Machine Learning #Deep Learning

ML Algorithms: Supervised Learning Tip to Tail

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.

#Machine Learning #Supervised Learning

ML: Supervised Learning An Introduction

By utilizing modern Python libraries, investigating machine learning tools, and delving into logistic regression, decision trees, and linearly inseparable data, you can master AI with our course.

#Hyperparameter #sklearn