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

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Applied Machine Learning Without Coding

This course is part of No-Code Data Science and Machine Learning Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Classification Algorithms

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Build your subject-matter expertise

This course is part of the No-Code Data Science and Machine Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

What's included

10 videos5 readings4 assignments

10 videos Total 37 minutes

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

5 readings Total 55 minutes

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

4 assignments Total 33 minutes

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

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.

What's included

11 videos4 readings4 assignments

11 videos Total 41 minutes

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

4 readings Total 40 minutes

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

4 assignments Total 33 minutes

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

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.

What's included

9 videos4 readings4 assignments

9 videos Total 33 minutes

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

4 readings Total 40 minutes

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

4 assignments Total 33 minutes

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

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.

What's included

1 video1 reading2 assignments

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

160 Courses 149,681 learners

Offered by

Edureka

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Frequently asked questions

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.