Will I get hands-on practice using Orange?

es. The course emphasizes hands-on learning through practical exercises where you build, evaluate, and tune machine learning models using Orange’s visual interface.

What data sources will I learn to work with?

You will work with structured datasets provided within the course to build and evaluate regression and classification models using Orange.

What skills will I gain from this course?

You will gain skills in building no-code machine learning workflows, applying regression and classification algorithms, evaluating model performance, and designing end-to-end predictive analytics solutions using Orange.

Is this course suitable for beginners in data science?

Yes. The course starts with foundational concepts and gradually progresses to advanced modeling techniques, making it suitable for beginners.

How is my learning evaluated in this course?

Your learning is evaluated through module quizzes, hands-on assessments, and a final assessment that tests your ability to build and evaluate complete machine learning workflows.

How long will it take to complete the course?

The course is self-paced and can typically be completed within a few weeks, depending on your learning pace and time commitment.

What is Orange Data Mining?

Orange is a visual machine learning and data mining platform. It lets you build complete ML workflows by connecting components on a canvas — no programming required. It's widely used in academia, research, and business analytics.

How are the hands-on demonstrations structured?

All ML techniques — from building regression workflows to tuning classification models — are taught through detailed instructor video demonstrations. You're expected to install Orange on your own machine, follow along with the instructor in real time, and pause the video whenever you need to replicate a step or revisit a concept before moving on.

What is the difference between regression and classification?

Regression predicts continuous numerical outcomes such as sales figures or temperatures, while classification predicts categorical outcomes such as fraud or no fraud. Both model types are taught in full, with separate modules and hands-on demonstrations for each.

How will I evaluate model performance?

You'll use RMSE, MAE, and R² for regression models, and confusion matrices, precision, recall, F1-score, and ROC curves for classification models — all visualized and interpreted directly within Orange's workflow canvas.

What is hyperparameter tuning and will I learn it?

Yes. You'll learn how to systematically adjust model parameters in Orange to improve accuracy, reduce overfitting, and select the best-performing model. The tuning process is fully demonstrated so you can follow along and experiment with your own settings.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Applied Machine Learning Without Coding

本课程是 No-Code Data Science and Machine Learning 专项课程的一部分

位教师：Edureka

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Explain fundamental machine learning concepts, mathematical foundations, and the role of no-code tools in building analytical workflows.
Apply Orange Data Mining to build regression and classification models using visual, no-code workflows.
Analyze model performance using appropriate evaluation metrics to compare, select, and improve machine learning models.
Evaluate and optimize machine learning solutions by tuning parameters and designing end-to-end predictive workflows for real-world data.

您将获得的技能

您将学习的工具

Classification Algorithms

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 No-Code Data Science and Machine Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

Machine learning is no longer exclusive to developers. This course gives you the hands-on skills to build, evaluate, and optimize regression and classification models using Orange Data Mining — a powerful visual ML platform — without writing a single line of code.

Throughout this course, you'll move from core ML fundamentals and essential mathematics through to practical model building, evaluation, and tuning — all through an intuitive visual workflow interface designed for data professionals and business users alike. Every technique is demonstrated through clear, instructor-led video walkthroughs that you can follow along on your own Orange setup, pausing and replaying as needed to build confidence at every step. By the end of this course, you'll be able to: - Build and evaluate regression models using linear regression, SVMs, and Random Forests with visual Orange workflows. - Apply classification algorithms including logistic regression, decision trees, KNN, and Naive Bayes to solve real-world prediction problems. - Evaluate model performance using RMSE, MAE, R², confusion matrices, and ROC curves to compare and select optimal models. - Perform feature selection and hyperparameter tuning in Orange to improve model accuracy and generalization without coding. This course is designed for a diverse audience: aspiring data analysts, machine learning beginners, business analysts, domain experts, and non-technical professionals who want to explore predictive analytics through a no-code approach. Basic familiarity with data concepts and spreadsheets, is recommended before enrolling. Gain the confidence to build and interpret machine learning models that solve real business problems — all through an intuitive visual interface with Orange Data Mining.

Build a strong foundation in no-code data science by learning how to use Orange for visual data mining while developing core machine learning and mathematical concepts. Explore the Orange interface, widgets and workflow design, then strengthen your understanding of linear algebra, probability and optimization fundamentals. Gain conceptual clarity on machine learning types, model evaluation strategies and common pitfalls like overfitting, preparing you for practical modeling workflows in later modules.

涵盖的内容

10个视频5篇阅读材料4个作业

10个视频总计37分钟

Course Introduction 4分钟
What is Orange? Visual Programming for Data Science 4分钟
Orange Interface Widgets, Canvas and Workflow Concepts 4分钟
Hands-On: Installing Orange and Creating the First Workflow 4分钟
Basic Linear Algebra Vectors, Matrices and Simple Operations 4分钟
Hands-On: Probability Basics and Continuous Distributions 4分钟
Understanding Slopes and Optimization Gradients 3分钟
Machine Learning Fundamentals 4分钟
Overfitting, Underfitting, and Bias-Variance Tradeoff 4分钟
Train-Test Split, Cross-Validation and Model Selection 2分钟

5篇阅读材料总计55分钟

Course Outline: No-Code Machine Learning with Orange 10分钟
Introduction to Orange Data Mining 10分钟
Basic Mathematics for Machine Learning 10分钟
Introduction to Machine Learning Concepts 10分钟
Module Summary: Introduction to Orange, ML Foundations and Mathematics 15分钟

4个作业总计33分钟

Knowledge Check: Introduction to Orange, ML Foundations and Mathematics 15分钟
Practice Assignment: Introduction to Orange Data Mining 6分钟
Practice Assignment: Basic Mathematics for Machine Learning 6分钟
Practice Assignment : Introduction to Machine Learning Concepts 6分钟

Develop practical regression modeling skills by progressing from linear regression fundamentals to advanced algorithms such as Support Vector Machines and Random Forests. Learn how to select features, build and compare regression models in Orange and evaluate performance using industry-standard metrics like RMSE, MAE and R². Strengthen your ability to optimize models through hyperparameter tuning and residual analysis to produce accurate, reliable predictions.

涵盖的内容

11个视频4篇阅读材料4个作业

11个视频总计41分钟

Understanding Regression Types and Mathematics of Linear Regression 3分钟
Hands-On: Feature Selection for Linear Regression 4分钟
Hands-On: Building Linear Regression Models in Orange 4分钟
Support Vector Machines and Random Forest for Regression 3分钟
Hands-On: Building SVM Regression Models in Orange 3分钟
Hands-On: Building Random Forest Regression Models in Orange 3分钟
Regression Metrics: RMSE, MAE, R² Score and Model Evaluation 4分钟
Model Selection 5分钟
Hands-On: Model Evaluation and Residual Analysis in Orange 3分钟
Hyperparameter Tuning Concepts 5分钟
Hands-On: Hyperparameter Tuning in Orange 3分钟

4篇阅读材料总计40分钟

Linear Regression Fundamentals 10分钟
Advanced Regression: SVM and Random Forest 10分钟
Regression Evaluation and Hyperparameter Tuning 10分钟
Module Summary : Regression Modeling - Basic to Advanced 10分钟

4个作业总计33分钟

Graded Assignment: Regression Modeling: Basic to Advanced 15分钟
Practice Assignment: Linear Regression Fundamentals 6分钟
Practice Assignment : Advanced Regression: SVM and Random Forest 6分钟
Practice Assignment : Regression Evaluation and Hyperparameter Tuning 6分钟

Master classification techniques by building, evaluating and tuning models for categorical prediction problems. Start with core classification concepts and algorithms such as logistic regression, decision trees, KNN and Naive Bayes, then advance to SVM and Random Forest classifiers. Learn to interpret confusion matrices, ROC curves and performance metrics while applying hyperparameter tuning to select the best-performing models for real-world classification tasks.

涵盖的内容

9个视频4篇阅读材料4个作业

9个视频总计33分钟

Understanding Classification - Types and Mathematics of Classification Algorithms 3分钟
Hands-On: Building Logistic Regression and Decision Tree Classification Models in Orange 3分钟
Hands-On: K-Nearest Neighbors and Naive Bayes Classification Models 4分钟
Support Vector Machines and Random Forest for Classification 5分钟
Hands-On: Building SVM Classification Models in Orange 4分钟
Hands-On: Building Random Forest Classifiers and Feature Importance 4分钟
Confusion Matrix, ROC Curves and Classification Metrics 4分钟
Hands-On: Model Evaluation with ROC-AUC and Performance Comparison 4分钟
Hands-On: Hyperparameter Tuning and Final Model Selection 3分钟

4篇阅读材料总计40分钟

Classification Fundamentals and Basic Algorithms 10分钟
Advanced Classification : SVM and Random Forest 10分钟
Classification Evaluation and Hyperparameter Tuning 10分钟
Module Summary : Classification Modeling : Basic to Advanced 10分钟

4个作业总计33分钟

Graded Assignment: Classification Modeling: Basic to Advanced 15分钟
Practice Assignment : Classification Fundamentals and Basic Algorithms 6分钟
Practice Assignment : Advanced Classification: SVM and Random Forest 6分钟
Practice Assignment : Classification Evaluation & Hyperparameter Tuning 6分钟

Consolidate your learning by revisiting the complete no-code data science workflow, from data exploration and mathematical foundations to regression and classification modeling. Reinforce key concepts, modeling decisions, and evaluation techniques while demonstrating your ability to build end-to-end machine learning solutions using Orange through a final assessment.

涵盖的内容

1个视频1篇阅读材料2个作业

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

157 门课程 147,337 名学生

提供方

Edureka

从 Machine Learning 浏览更多内容

Edureka
No-Code Data Science and Machine Learning
专项课程
LearnQuest
No-Code Machine Learning and Data Science for Everyone
专项课程
LearnQuest
Practical Machine Learning without Coding
课程
Maven Analytics
Complete Visual Guide to Machine Learning
课程

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

This course is designed for aspiring data analysts, machine learning beginners, students, business analysts, and non-technical professionals who want to learn machine learning using a no-code, visual approach with Orange.

The course covers Orange Data Mining fundamentals, basic mathematics for machine learning, regression and classification modeling, model evaluation, and hyperparameter tuning using visual workflows.

No. The course is fully no-code and does not require any prior programming or machine learning experience.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.