No-Code ML: Build & Deploy AI with Dataiku

अवलोकन

Master the fundamentals of machine learning and leverage no-code tools to build, train, and deploy powerful AI models What you'll learn:

Explain key machine learning concepts and no-code tools for data preprocessing, model building, and deployment.Build and deploy machine learning models using no-code platforms through guided demos and real-world examples.Enhance AI Trustworthiness by exploring model interpretability, detecting bias, and ensuring fairness in ML models through no-code tools and fairness reports.Apply no-code machine learning techniques to generate model fairness reports and monitor performance for continuous improvement. Are you eager to dive into the world of machine learning but wary of complex coding?

This course is your gateway to understanding and applying machine learning concepts—without writing a single line of code. Designed for beginners and professionals alike, you’ll explore both the theory and practical applications of machine learning through a dynamic blend of lectures and hands-on demos.What You’ll Learn:

Core Concepts & Foundations:

Gain a thorough grounding in machine learning fundamentals, including an overview of deep learning, the differences between ML and DL, and the key components that drive these technologies.

Explore the nuances between rule-based and data-driven systems and understand how to define problems and collect data effectively.Data Preparation & Model Building:

Learn essential data preprocessing techniques such as normalization, standardization, and feature engineering. Dive into practical demos using platforms like Kaggle and Dataiku to see real-world applications—from model building and training to evaluation techniques including confusion matrices, ROC curves, and more.No-Code Tools & Deployment:

Discover the transformative power of no-code machine learning tools.

Understand how to build, test, deploy, and monitor models seamlessly without traditional programming. Explore advanced topics such as model fairness and learn to generate comprehensive model fairness reports.Who Should Enroll:

Aspiring Machine Learning Enthusiasts:

If you’re new to machine learning and want a clear, accessible introduction without the coding barrier, this course is for you.Data Analysts & Professionals:

Enhance your skill set by learning to implement and deploy machine learning solutions quickly using no-code platforms.Business Leaders & Innovators:

Gain insights into leveraging AI to drive better decision-making and innovation within your organization.By the end of this course, you’ll be equipped with the knowledge and practical skills to create robust machine learning models using intuitive, no-code platforms.

Whether you’re aiming to upskill in your current role or pivot into the rapidly growing field of AI, this course will empower you to transform data challenges into strategic opportunities. Enroll now and take your first step toward mastering the future of technology—all without writing a single line of code!

पाठ्यक्रम

• Introduction to No-Code Machine Learning
Course Overview and Objectives
Introduction to No-Code Platforms
Understanding the Importance of Machine Learning
• Core Concepts & Foundations
Overview of Machine Learning vs. Deep Learning
Key Components of ML and DL Technologies
Differences Between Rule-Based and Data-Driven Systems
Problem Definition and Data Collection Strategies
• Data Preparation & Model Building
Data Preprocessing: Normalization and Standardization
Feature Engineering Techniques
Introduction to Kaggle and Dataiku for No-Code ML
• Practical Demos: Model Building and Evaluation
Step-by-Step Model Building on No-Code Platforms
Training Models: Best Practices and Techniques
Model Evaluation Techniques: Confusion Matrices, ROC Curves
• No-Code Tools & Deployment
Overview of No-Code Machine Learning Tools
Building, Testing, and Deploying Models Without Code
Monitoring Model Performance Post-Deployment
• Enhancing AI Trustworthiness
Understanding Model Interpretability
Detecting and Mitigating Bias in ML Models
Ensuring Fairness and Generating Fairness Reports
• Advanced Topics in No-Code Machine Learning
Comprehensive Model Fairness Reports
Continuous Monitoring for Model Improvement
Real-World Applications and Case Studies
• Who Should Enroll
Aspiring Machine Learning Enthusiasts
Data Analysts & Professionals
Business Leaders & Innovators
• Conclusion and Next Steps
Recap of Key Learnings
Additional Resources for Further Learning
Course Wrap-Up and Closing Remarks

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द्वारा पढ़ाया गया

Learnsector LLP and Rajnish Tandon

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विषय

Computer Science