Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME
Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME
ISBN: 9780135405253 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 4h 46m | 1.36 GB
Instructor: Dursun Delen
ISBN: 9780135405253 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 4h 46m | 1.36 GB
Instructor: Dursun Delen
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Introduction
Data Science Made Easy: Introduction
Lesson 1: Data Science Overview
Topics
1.1 Definition, Terminology, and a Simple Taxonomy
1.2 Data Science Process
1.3 Data Science Methods and Algorithms
1.4 AI/ML Evolution
Lesson 2: Data Science Tools
Topics
2.1 Tool Landscape
2.2 Introduction to KNIME AP
2.3 Nodes and Extensions
2.4 KNIME Demo with Iris Dataset—Part 1
2.5 KNIME Demo with Iris Dataset—Part 2
Lesson 3: ML Model Development with KNIME
Topics
3.1 Data Ingestion and Preparation—Part 1
3.2 Data Ingestion and Preparation—Part 2
3.3 ML Model Building and Testing
3.4 Comparative Assessment
Lesson 4: Best Practices in Data Science and AI/ML
Topics
4.1 Data Balancing for Class Imbalance Problem
4.2 Cross Validation for Bias-Variance Tradeoff
4.3 Model Ensembles (with Bagging Boosting)
4.4 Model Explainability (XAI)
Lesson 5: Text Analytics
Topics
5.1 Overview of Text Mining and Natural Language Processing (NLP)
5.2 Text Mining Process
5.3 TM Applications―Sentiment Analysis
5.4 TM Applications―Topic Modeling
Summary
Data Science Made Easy: Summary
Data Science Made Easy: Introduction
Lesson 1: Data Science Overview
Topics
1.1 Definition, Terminology, and a Simple Taxonomy
1.2 Data Science Process
1.3 Data Science Methods and Algorithms
1.4 AI/ML Evolution
Lesson 2: Data Science Tools
Topics
2.1 Tool Landscape
2.2 Introduction to KNIME AP
2.3 Nodes and Extensions
2.4 KNIME Demo with Iris Dataset—Part 1
2.5 KNIME Demo with Iris Dataset—Part 2
Lesson 3: ML Model Development with KNIME
Topics
3.1 Data Ingestion and Preparation—Part 1
3.2 Data Ingestion and Preparation—Part 2
3.3 ML Model Building and Testing
3.4 Comparative Assessment
Lesson 4: Best Practices in Data Science and AI/ML
Topics
4.1 Data Balancing for Class Imbalance Problem
4.2 Cross Validation for Bias-Variance Tradeoff
4.3 Model Ensembles (with Bagging Boosting)
4.4 Model Explainability (XAI)
Lesson 5: Text Analytics
Topics
5.1 Overview of Text Mining and Natural Language Processing (NLP)
5.2 Text Mining Process
5.3 TM Applications―Sentiment Analysis
5.4 TM Applications―Topic Modeling
Summary
Data Science Made Easy: Summary
Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME
