No-Code ML: Build & Deploy AI Without Coding from Udemy

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.

What's inside

Learning objectives

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.

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.

Syllabus

Master core ML fundamentals: understand no-code AI, data preprocessing, model building, deep learning, and evaluation with hands-on demos.

Introduction

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a clear, accessible introduction to machine learning, removing the coding barrier and making it ideal for those new to the field

Enhances skill sets by teaching how to implement and deploy machine learning solutions rapidly using no-code platforms, which can be valuable for professionals

Offers insights into leveraging AI for improved decision-making and innovation, which is beneficial for leaders aiming to integrate AI into their strategies

Covers data preprocessing techniques like normalization and feature engineering, which are essential for preparing data for machine learning models

Explores model fairness and generating fairness reports, which is crucial for ensuring ethical and unbiased AI applications in professional settings

Relies on platforms like Kaggle and Dataiku, so learners should ensure that they are comfortable using these platforms and that they meet their needs

Reviews summary

No-code ml: practical introduction and deployment

According to learners, this course offers a clear and accessible introduction to machine learning concepts, fulfilling its promise to teach AI without coding. Many found it highly suitable for beginners and those new to the field. The course is praised for its practical demos, especially the deep dive into using the Dataiku platform for building and deploying models. While the core concepts are explained well, some early users reported issues with tool setup, particularly with Dataiku or Kaggle demos being outdated or difficult to get running. However, more recent reviews strongly highlight the effectiveness of the Dataiku section, suggesting this key part of the course is well-received and functional for most.

Accessible entry point for newcomers to ML.

"Perfect for someone with no coding background looking to understand and apply ML."

"A solid introduction to ML for beginners that doesn't require prior technical expertise."

"Highly recommended for beginners in AI who are intimidated by programming."

"It made the field approachable for me as a complete novice."

Demos and practical steps are very helpful.

"The demos were very helpful in seeing the concepts applied in real-world scenarios using the platforms."

"Seeing the steps demonstrated on Dataiku made it click and much easier to follow along."

"I liked the hands-on aspect using the tools, even if some parts were watching."

"The practical parts cemented my understanding gained from the lectures."

Explains core ML ideas clearly and simply.

"The course explains complex concepts clearly, making it accessible even without prior ML knowledge."

"It really helped me understand the core principles of ML and how different algorithms work."

"The theoretical aspects were explained well before diving into the practical parts."

"I appreciated how the instructor broke down potentially confusing topics."

Delivers on the promise of no-code ML via Dataiku.

"This course delivers on the no-code promise, focusing on practical application using tools like Dataiku."

"I learned how to build and deploy models on Dataiku effectively, which was the main goal."

"A fantastic way to get started with no-code AI tools; Dataiku section is a highlight."

"The walkthroughs of Dataiku were exactly what I needed to see how no-code ML works."

Some users report difficulties setting up tools.

"Had issues getting the Dataiku part to work correctly due to setup or software version conflicts."

"The Kaggle kernel issues mentioned in one demo made that specific part impossible to follow."

"Some demos were hard to replicate due to problems installing or configuring the necessary platforms."

"Encountered problems with the software versions needed for the Dataiku exercises."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in No-Code ML: Build & Deploy AI Without Coding with these activities:

Review Machine Learning Fundamentals

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Solidify your understanding of core machine learning concepts before diving into the no-code approach.

Browse courses on Machine Learning Fundamentals

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Review the different types of machine learning algorithms.
Understand the concepts of supervised and unsupervised learning.
Familiarize yourself with common evaluation metrics.

Follow No-Code ML Tutorials

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Refine your skills by following tutorials on specific no-code machine learning platforms.

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Choose a no-code platform covered in the course.
Find tutorials on building specific types of models.
Follow the tutorials step-by-step.
Experiment with different settings and parameters.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of machine learning algorithms and techniques to complement the no-code approach.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters related to model evaluation and selection.
Focus on the sections explaining different machine learning algorithms.

Four other activities

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Build a Classification Model with a No-Code Platform

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Apply the concepts learned in the course by building a classification model using a no-code platform.

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Choose a dataset from Kaggle or another public source.
Import the data into a no-code machine learning platform.
Preprocess the data using the platform's built-in tools.
Build and train a classification model.
Evaluate the model's performance using appropriate metrics.

Create a Blog Post on No-Code ML

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Solidify your understanding by explaining the benefits and applications of no-code machine learning in a blog post.

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Research the current landscape of no-code machine learning tools.
Identify the key benefits of using no-code platforms.
Describe real-world applications of no-code ML.
Write a blog post summarizing your findings.

Develop a Model Fairness Report

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Deepen your understanding of model fairness by creating a comprehensive report using no-code tools.

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Select a dataset with potential bias issues.
Build a machine learning model using a no-code platform.
Use the platform's fairness tools to generate a report.
Analyze the report and identify potential biases.
Document your findings and recommendations.

Read 'Interpretable Machine Learning' by Christoph Molnar

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Enhance your understanding of model interpretability and fairness, crucial for responsible AI development.

View Interpretable Machine Learning on Amazon

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Focus on chapters related to model explanation techniques.
Understand the different methods for assessing model fairness.

Career center

Learners who complete No-Code ML: Build & Deploy AI Without Coding will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

As a Machine Learning Engineer, you are responsible for developing, deploying, and maintaining machine learning models. This course provides a foundation in machine learning concepts and model building using no-code platforms, which can accelerate the development and deployment process. This course helps one understand core components of machine learning and how to use tools like Kaggle and Dataiku. The course emphasizes practical applications and model evaluation techniques, which a Machine Learning Engineer will find immediately useful. The no-code approach covered in this course helps one learn to rapidly prototype and deploy models. By learning to build, test, and deploy models without traditional programming, the Machine Learning Engineer can focus on refining model performance. Learning to detect bias and ensure fairness in models, as covered in this course, are crucial for ethical AI implementation.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

The role of a Data Scientist involves analyzing large datasets, building predictive models, and extracting actionable insights to drive business decisions. This course helps one learn essential data preprocessing techniques, model building, and evaluation using no-code platforms. With an understanding of machine learning fundamentals, one can leverage these tools to quickly explore data, build models, and test hypotheses using a no-code approach. This course covers data preprocessing, model building, and deployment, which can be directly applied to the Data Scientist's projects. You can use the no-code tools covered to rapidly prototype and deploy models and use the insights gained to communicate findings to stakeholders. This course also covers model fairness and bias detection. As a Data Scientist, you can use this knowledge to ensure ethical AI implementations.

See salaries and explore the career path for Data Scientist

AI Developer

An AI Developer builds and implements artificial intelligence solutions across various applications. This course introduces machine learning concepts and provides practical experience with no-code tools. One can learn how to build, test, and deploy AI models without writing code. The course's focus on no-code deployment and monitoring aligns with the AI Developer's need for efficient and scalable solutions. The materials covering model fairness are vital for ensuring ethical AI practices. Understanding feature engineering, model selection, and deployment strategies, as taught in this course, provides a practical advantage for an AI Developer. The no-code approach helps AI Developers swiftly test different algorithms and deployment strategies, leading to faster iteration and innovation.

See salaries and explore the career path for AI Developer

Business Intelligence Analyst

As a Business Intelligence Analyst, one transforms data into insights that inform strategic decisions. This course helps one learn machine learning fundamentals covering data preprocessing, model building, and deployment. With the no-code tools taught in this course, the Business Intelligence Analyst can rapidly analyze data, build predictive models, and identify trends without being hindered by coding complexities. The course helps one learn to preprocess data, build machine learning models, and generate reports. The knowledge of model evaluation and validation gained from this course helps the Business Intelligence Analyst ensure the accuracy and reliability of their findings. Exploring model fairness and bias detection will enable a more responsible and ethical analysis of business data.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Consultant

As a Machine Learning Consultant, one advises organizations on how to leverage machine learning to solve business problems. This course helps the Machine Learning Consultant learn to apply machine learning concepts without needing to write code. One can use this to quickly demonstrate the value of machine learning to clients, rapidly prototype solutions, and communicate technical concepts to non-technical stakeholders. The Machine Learning Consultant can also use this course to learn core machine learning fundamentals and how to use tools like Kaggle and Dataiku. The course highlights the importance of model fairness, and this is a crucial consideration for any organization implementing machine learning.

See salaries and explore the career path for Machine Learning Consultant

Data Analyst

A Data Analyst collects, processes, and performs statistical analyses of data. The insights from this work help improve business operations. This course provides a solid foundation in machine learning principles and allows one to rapidly prototype and deploy machine learning solutions. This course helps one learn data preprocessing techniques such as normalization and standardization. The course's practical demos using platforms like Kaggle and Dataiku help one apply what they learn to real-world scenarios. Data Analysts can use this course to quickly enhance their skillset and create more sophisticated analyses. The course's coverage of model fairness and bias detection helps Data Analysts address ethical considerations in their work.

See salaries and explore the career path for Data Analyst

AI Product Manager

The AI Product Manager is responsible for guiding the vision, strategy, and roadmap for AI-powered products. This course can provide the AI Product Manager with a strong understanding of machine learning concepts, model building, and deployment processes, even without coding expertise. This course helps one understand core components of machine learning and learn how to use tools like Kaggle and Dataiku. The course highlights the importance of model fairness and bias detection. Understanding these aspects of AI development allows the AI Product Manager to make informed decisions about product features. This course may be useful for an AI Product Manager as they make product decisions and explore new product possibilities.

See salaries and explore the career path for AI Product Manager

Statistical Modeler

The Statistical Modeler develops and applies statistical models to solve business problems. This course provides foundational knowledge in machine learning and model building using no-code platforms. A Statistical Modeler can learn to preprocess data, build machine learning models, and evaluate results. This course emphasizes practical applications and model evaluation techniques, which are directly applicable to the Statistical Modeler's work. Learning about model fairness can also help ensure ethical and responsible AI practices. The techniques taught would be useful for rapid model development.

See salaries and explore the career path for Statistical Modeler

Data Engineer

The Data Engineer is responsible for building and maintaining the infrastructure required for data storage, processing, and analysis. This course may be useful, as the Data Engineer can learn to preprocess data, build machine learning models, and deploy machine learning pipelines using no-code tools. The course's coverage of data preprocessing techniques, model deployment strategies, and monitoring tools could be valuable for the Data Engineer. This course also covers the use of API services, which a Data Engineer might use to integrate machine learning models into existing systems. The knowledge of model fairness and bias detection gained from this course helps the Data Engineer ensure the responsible and ethical deployment of AI solutions.

See salaries and explore the career path for Data Engineer

AI Solutions Architect

The AI Solutions Architect designs and implements AI solutions that meet specific business needs. This course may be useful, as it provides the AI Solutions Architect with a practical understanding of machine learning concepts and model deployment processes, even without coding skills. This course emphasizes data preprocessing, model building, and deployment using no-code platforms, which can help the AI Solutions Architect quickly prototype and validate solutions. This course also covers model fairness and bias detection, which can help the AI Solutions Architect design ethical and responsible AI systems. This course also covers machine learning fundamentals and how to use tools like Kaggle and Dataiku.

See salaries and explore the career path for AI Solutions Architect

Financial Analyst

A Financial Analyst analyzes financial data, provides forecasts, and helps guide investment decisions. This course can assist the Financial Analyst in understanding core machine learning concepts and model building, even without coding. One can use the no-code tools covered to rapidly prototype and deploy models and use the insights gained to communicate findings to stakeholders. This course also covers model fairness and bias detection. As a Financial Analyst, you can use this knowledge to ensure ethical AI implementations. This course may be useful in your daily workflow.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

A Quantitative Analyst, often working in the financial sector, develops and implements mathematical models for pricing, risk management, and trading strategies. This course may be useful, as it can help the Quantitative Analyst learn machine learning techniques. This course covers data preprocessing, model building, and deployment using no-code platforms. By learning machine learning fundamentals and how to use tools like Kaggle and Dataiku, the Quantitative Analyst can explore new approaches to model building and gain insights from data. The course also covers model fairness and bias detection. The Quantitative Analyst can ensure the responsible and ethical deployment of AI solutions.

See salaries and explore the career path for Quantitative Analyst

Bioinformatician

A Bioinformatician analyzes biological data using computational tools and techniques. While coding skills are usually essential, this course may be useful for learning the basics of machine learning. This course covers data preprocessing, model building, and deployment using no-code platforms. By learning machine learning fundamentals and how to use tools like Kaggle and Dataiku, the Bioinformatician can explore AI for biological data analysis. The course also covers model fairness and bias detection. The Bioinformatician can ensure the responsible and ethical deployment of AI solutions. The techniques taught would be useful for rapid model development.

See salaries and explore the career path for Bioinformatician

Market Research Analyst

As a Market Research Analyst, one studies market conditions to examine potential sales of a product or service. This course may be useful. The Market Research Analyst can use the course to rapidly prototype and deploy machine learning solutions. The course helps one learn data preprocessing techniques such as normalization and standardization. The course's practical demos using platforms like Kaggle and Dataiku help one apply what they learn to real-world scenarios. Market Research Analysts can use this course to quickly enhance their skillset and create more sophisticated analyses. The course's coverage of model fairness and bias detection helps Market Research Analysts address ethical considerations in their work.

See salaries and explore the career path for Market Research Analyst

Healthcare Analyst

The Healthcare Analyst analyzes healthcare data to improve outcomes and efficiency. This course may be useful as the Healthcare Analyst learns the basics of machine learning. This course covers data preprocessing, model building, and deployment using no-code platforms. By learning machine learning fundamentals and how to use tools like Kaggle and Dataiku, the Healthcare Analyst can explore new tools for analysis. The course also covers model fairness and bias detection. The Healthcare Analyst can ensure the responsible and ethical deployment of AI solutions.

See salaries and explore the career path for Healthcare Analyst

No-Code ML

Build & Deploy AI Without Coding

What's inside

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Syllabus

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Reviews summary

No-code ml: practical introduction and deployment

Activities

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