AutoML BootCamp: No Code Machine Learning

No Code Machine Learning

Raj Chhabria

Last Updated on October 12, 2023 by GeeksGod

Course : AutoML Automated Machine Learning BootCamp (No Code ML)


“No code” machine learning (ML) refers to the use of ML platforms, tools, or libraries that allow users to build and deploy ML models without writing any code. This approach is intended to make ML more accessible to a wider range of users, including those who may not have a strong programming background.

Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker also includes built-in algorithms, pre-built libraries for common machine learning tasks, and a variety of tools for data pre-processing, model tuning, and model deployment. SageMaker also integrates with other AWS services to provide a complete machine learning environment.

AutoML in SageMaker

AutoML in SageMaker refers to the automatic selection and tuning of machine learning models to improve the accuracy and performance of the models. This can be done by using SageMaker’s built-in algorithms and libraries or by using custom algorithms and libraries. SageMaker also includes a feature called Automatic Model Tuning which allows for tuning of the hyper-parameters of the models to improve their performance.

SageMaker Studio Canvas

SageMaker Studio Canvas is a feature that allows users to interact with their data, build and visualize workflows, and create, run, and debug Jupyter notebooks, all within the same web-based interface. The Canvas provides a visual and interactive way to explore, manipulate and visualize data, and allows users to create Jupyter notebooks and drag-and-drop pre-built code snippets, called “recipes” to quickly perform common data pre-processing, data visualization, and data analysis tasks.

SageMaker Studio Canvas also allows users to easily share their notebooks, recipes, and data with other users and collaborate on projects. This helps to simplify the machine learning development process, accelerate the development of machine learning models, and improve collaboration among teams.

Benefits of “No code” Machine Learning with Amazon SageMaker

Using “No code” machine learning with Amazon SageMaker brings several advantages:

1. Accessibility for Non-programmers

By eliminating the need for coding, “No code” machine learning makes it possible for individuals without strong programming backgrounds to participate in building and deploying ML models. This widens the pool of potential users and encourages inclusivity in the field of machine learning.

2. Faster Development Time

With pre-built algorithms, libraries, and tools available in Amazon SageMaker, users can save time that would otherwise be spent on coding and focus more on the specific tasks of building, training, and deploying ML models. This accelerates the development process and enables faster iterations and experimentation.

3. Automated Model Selection and Tuning

AutoML in SageMaker automates the process of selecting and tuning machine learning models. This helps improve the accuracy and performance of the models without requiring users to manually try different combinations of hyper-parameters. The Automatic Model Tuning feature further enhances the performance of the models by optimizing the hyper-parameters.

4. Visual and Interactive Data Exploration

The SageMaker Studio Canvas provides a visual and interactive way to explore, manipulate, and visualize data. Users can easily navigate and interact with their datasets, which helps in gaining insights and understanding patterns in the data. This visual exploration aids in the decision-making process when building ML models.

5. Collaborative Development and Sharing

SageMaker Studio Canvas allows users to easily share their notebooks, recipes, and data with other users. This promotes collaboration among teams and facilitates knowledge sharing. Multiple users can work together on a project, making it easier to develop ML models collectively and leverage the expertise of different team members.


In conclusion, “No code” machine learning with Amazon SageMaker offers a user-friendly and efficient way to build, train, and deploy ML models. With its built-in algorithms, libraries, and automated model selection and tuning capabilities, it simplifies the development process and improves the performance of the models. The visual and interactive features of SageMaker Studio Canvas enhance data exploration, while the collaborative development and sharing options foster teamwork and knowledge exchange. By optimizing ML accessibility and productivity, Amazon SageMaker contributes to the advancement of the field and encourages a wider adoption of machine learning technologies.

Free Udemy Coupon and AutoML

If you are interested in learning more about “No code” machine learning with Amazon SageMaker and want to enhance your skills in AutoML, you can take advantage of Free Udemy Coupons that provide access to various online courses. These courses offer comprehensive instruction and hands-on experience in utilizing the capabilities of SageMaker and AutoML to their full potential.

Selecting the Right Free Udemy Course for AutoML

When choosing a Free Udemy Course related to AutoML, it is essential to consider the course content, instructor credibility, and student reviews. Look for courses that specifically cover topics such as SageMaker Studio Canvas, automated model selection and tuning, and data exploration using visual interfaces. Make sure the course provides practical examples and real-world scenarios to enhance your learning experience.

Benefits of Free Udemy Coupon for Learning AutoML

Utilizing Free Udemy Coupons to learn AutoML can be highly beneficial. It enables you to expand your knowledge and skills in a cost-effective manner. By taking advantage of these coupons, you can access high-quality learning materials and explore cutting-edge techniques in AutoML without any financial burden.

So, don’t miss out on the opportunity to enhance your expertise in AutoML and leverage the power of “No code” machine learning with Amazon SageMaker. Grab a Free Udemy Coupon today and embark on your journey towards becoming a proficient practitioner in AutoML and machine learning.

Udemy Coupon :


What you will learn :

1. Understanding the Lifecycle of a Machine Learning Project.
2. Introduction to Cloud Computing and how to use Cloud Computing for Machine Learning.
3. Learn about AWS SageMaker Canvas.
4. Perform Diabetes Prediction Machine Learning Practical on AWS SageMaker Canvas without writing a single line of code.

100% off Coupon