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No-Code AI & ML: From Data to Deployment Without Coding
Bewertung: 4,6 von 5(134 Bewertungen)
1.026 Teilnehmer:innen
Zuletzt aktualisiert 9/2025
Englisch

Das wirst du lernen

  • 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.

Kursinhalt

4 Abschnitte56 Lektionen7 Std. 6 Min. Gesamtdauer
  • Introduction2:51
  • Overview and Importance of No-Code Machine Learning6:33
  • Scope of Machine Learning2:12
  • Core Components of Machine Learning2:32
  • Performance Metrics8:53
  • Introduction to Deep Learning8:49
  • Comparison of ML and DL4:37
  • Applications of AI, ML, and DL15:05
  • Types of Machine Learning4:55
  • Rule-based vs Data-driven System8:45
  • Model Fit9:57

Anforderungen

  • No technical background or programming experience is required. This course is designed to guide you through every step of no-code machine learning.

Beschreibung

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!

Für wen eignet sich dieser Kurs:

  • Beginners eager to explore machine learning without prior coding experience
  • Business professionals and decision-makers looking to harness AI for strategic insights
  • Data enthusiasts and analysts who want to build and deploy models using intuitive, no-code tools
  • Anyone curious about AI who prefers hands-on, practical training over complex programming