Data Science Delight: No-Code Machine Learning Mastery with Orange

About Course

Welcome to “Data Science Delight: No-Code Machine Learning Mastery with Orange,” a comprehensive and interactive course designed to demystify the world of data science and machine learning using the powerful, user-friendly tool, Orange. This course is perfect for anyone who wants to harness the power of data without needing to write a single line of code.

Fundamentals of data science and its applications
Data loading, cleaning, and preprocessing techniques
Data transformation and feature engineering
Basic and advanced machine learning concepts
Supervised and unsupervised learning methods
Model evaluation, selection, and optimization
Practical applications of machine learning
Hands-on project work and real-world data analysis

Course Content

Introduction to Data Science and Orange Basics

Overview of data science
Importance and applications

Installing Orange and Basic Data Analysis

Data Science Fundamentals – Data Loading and Preprocessing

Data Transformation Techniques

Introduction to Machine Learning

Supervised Learning in Orange – Part 1

Supervised Learning in Orange – Part 2

Model Evaluation and Selection

Unsupervised Learning and Feature Engineering

Feature Selection and Transformation

Advanced Topics in Machine Learning

Model Optimization and Hyperparameter Tuning

Real-world Applications and Project Work

Practical Machine Learning Project

Project Presentations and Course Conclusion

Course Review and Q&A

About Course

What Will You Learn?

Course Content

Introduction to Data Science and Orange Basics

Overview of data science

Importance and applications

Installing Orange and Basic Data Analysis

Installing Orange

Navigating Orange interface

Loading datasets and basic data analysis

Data Science Fundamentals – Data Loading and Preprocessing

Loading datasets

Data cleaning and preprocessing

Data Transformation Techniques

Importance of data transformation

Common data transformation techniques

Introduction to Machine Learning

Introduction to Machine Learning

Types of machine learning

Common algorithms

Supervised Learning in Orange – Part 1

Building and evaluating classification models

Training and testing models in Orange

Supervised Learning in Orange – Part 2

Supervised Learning and Model Evaluation

Regression analysis in Orange

Evaluating regression models

Model Evaluation and Selection

Cross-validation techniques

Comparing and selecting models

Unsupervised Learning and Feature Engineering

Clustering techniques

Association rule mining

Feature Selection and Transformation

Importance of feature selection

Data transformation techniques

Advanced Topics in Machine Learning

Deep Learning and Neural Networks Basics

Introduction to neural networks

Deep learning concepts

Model Optimization and Hyperparameter Tuning

Hyperparameter tuning techniques

Model optimization strategies

Real-world Applications and Project Work

Data Preprocessing for Advanced Analysis

Advanced data preprocessing techniques

Practical Machine Learning Project

Applying machine learning techniques to real-world datasets

Project development and presentations

Project Presentations and Course Conclusion

Project Presentations – Peer presentations of projects

Project Presentations – Feedback and discussion

Course Review and Q&A

Recap of key concepts and QA session