10 Days of No Code Artificial Intelligence Bootcamp
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Unclaimed ProfileThe 10 Days of No Code Artificial Intelligence Bootcamp designed by Prof. Ryan Ahmed is a game-changing program tailored for professionals, entrepreneurs, and tech enthusiasts who want to harness the power of machine learning without writing a single line of code. Over a structured ten-day journey, this course demystifies advanced AI concepts and guides students through building ten distinct real-world projects. By leveraging state-of-the-art visual and automated machine learning platforms, participants gain hands-on experience with industry-standard tools including Google Teachable Machines, DataRobot, AWS SageMaker Autopilot, and Google Vertex AI. Throughout this practical bootcamp, learners explore critical machine learning workflows, from data preparation to model deployment and monitoring. You will build and deploy models targeting diverse scenarios, such as detecting diabetic retinopathy, classifying fashion items, predicting employee attrition, and estimating used car prices. Beyond just clicking buttons, the course ensures a deep conceptual understanding of fundamental AI principles. You will learn how to evaluate models using key performance indicators like confusion matrices, R-squared, and mean absolute error, and understand the core theory behind deep neural networks like ResNets. Whether you are a business analyst looking to make data-driven decisions or a product manager wanting to integrate AI features, this course provides the perfect launchpad.
About the creator
Prof. Ryan Ahmed
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Prof. Ryan Ahmed is a distinguished educator and technology leader dedicated to making artificial intelligence and machine learning accessible to a global audience. With a profound understanding of cutting-edge tech, Ryan specializes in demystifying complex concepts such as Agentic AI engineering, LLM system architecture, and advanced retrieval-augmented generation (RAG). His teaching methodology centers on a practical, hands-on approach that bridges the gap between theoretical data science and enterprise-ready application development. Whether guiding his students through building custom Python-based AI agents from scratch or configuring enterprise workflows using Microsoft Copilot Studio, Ryan ensures every lesson is grounded in real-world utility. He…Show more
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Learning format
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What You'll Learn
- Build, train, test and deploy 10 AI/ML models in 10 days without writing any code.
- Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine.
- Visualize State-of-the-Art Artificial Intelligence Models Using Tensorspace JS, Google Tensorflow Playground and Ryerson 3D CNN Visualizations.
- Explain the difference between learning rate, epochs, batch size, accuracy, and loss.
- Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI.
- Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI.
- Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate.
- Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.
- Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity).
- Predict employee attrition based on their features such as employee engagement, distance from home, job satisfaction using DataRobot AI.
- Develop an AI model to detect face masks using Google Teachable Machines.
- Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot.
- Learn how to transfer knowledge from a pre-trained Artificial Neural Network to a new network using transfer learning strategy.
- Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in DataRobot.
- Learn how to use SageMaker Studio AutoML tool to build, train and deploy AI/ML models which requires almost zero coding experience.
- Differentiate between various regression models KPIs such as R2 or coefficient of determination, Mean Absolute Error and Mean Squared error.
- Learn how to build, train, test and deploy advanced machine learning classification models using Google Vertex AI.
- Understand how to leverage the power of AI/ML to predict bank customers credit card default using their features such as interest rates and loan purpose
- Learn how to create a new dataset using Google Vertex AI Develop and manage experiments using Google Vertex AI.
- Understand the theory, intuition, and mathematics behind simple and multiple linear regression and differentiate between various regression models KPIs.
- Deploy the best model after the hyperparameters optimization job is complete and Learn how to assess feature importance and explain model predictions.
- Deploy and monitor AI/ML models and create AI/ML applications with Google Vertex AI.
Best For
- Business analysts and managers looking to implement AI solutions without learning programming.
- Entrepreneurs wanting to build and prototype AI-driven product features quickly.
- Non-technical professionals who need to understand AI model evaluation and deployment workflows.
- Tech enthusiasts eager to explore modern AutoML platforms like DataRobot and AWS SageMaker.
Not For
- Software engineers or data scientists looking to build custom models using Python or R.
- Individuals seeking an in-depth mathematical or programming foundation in machine learning algorithms.
- Users who prefer learning by writing and debugging code rather than using visual interfaces.
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