No Code - Low Code Machine Learning For Healthcare

Explore the fundamentals of artificial intelligence, machine learning, and deep learning in healthcare, including supervised and unsupervised methods, computer vision applied to imaging, and multi-omics data for personalized predictions.

Explore essential resources to accelerate your AI and machine learning journey in healthcare, including Google, YouTube, Medium, Toward Data Science, GitHub, and Google Colab with AutoGluon and Python.

Learn the basics of Python programming for data science and healthcare, using Google Colab and Jupyter notebooks, and explore essential libraries like NumPy and Pandas.

Learn to perform real data science and machine learning in Python by googling, using Colab and pandas to load, describe, and prepare heart disease data via renaming and dropping columns.

Explore Codex, an OpenAI model that turns text into code in Python for machine learning, enabling natural language to Python and text-to-command workflows for healthcare applications.

Explore the basics of machine learning in healthcare, covering supervised and unsupervised learning, key algorithms (decision trees, SVM, random forest, XGBoost, PCA), model evaluation, explainability, and low-code/no-code lab projects.

Explore explainability in healthcare machine learning, revealing how and why AI decisions occur, addressing bias, robustness, and transferability, with Shap values and heatmaps for structured data and imaging.

Evaluate machine learning models in healthcare using classification and regression metrics, confusion matrices, and precision/recall trade-offs; address imbalanced data, benchmark against current standards, and avoid overfitting for clinical usefulness.

Build machine learning models with low-code tools on healthcare data, using pandas, Lightgbm, XGBoost, and Autogluon. Explain predictions with SHAP and evaluate with accuracy, confusion matrix, and F1.

Explore building machine learning models with no-code using canvas on AWS, including data preparation in S3, quick model builds, feature engineering, and single or batch predictions.

Explore the basics of deep learning algorithms and their healthcare applications, including convolutional neural networks, recurrent neural networks, natural language processing, and a lab for low-code, no-code models.

Explore the mechanics of convolutional neural networks and recurrent neural networks, including transfer learning, in healthcare imaging and time-sequence data, with practical limits and challenges.

Explore how natural language processing enables machines to read, understand, and derive meaning from human language, and apply NLP to healthcare, EMR data, literature search, and chatbots.

Master no-code and low-code deep learning for healthcare using AutoML tools. Build models with transfer learning and data preparation, train and evaluate with accuracy and AUC.

Explore AWS AI services for no-code deep learning, including SageMaker Studio and Jump Start, to train, deploy, and manage models with Data Wrangler, S3, and feature store.

Explore bias and fairness in healthcare machine learning, identify data and modeling biases, differentiate bias from fairness, and learn strategies to improve patient access and outcomes.

Explore the difference between repeatability and reproducibility in health care machine learning, why reproducible results matter, and strategies like diverse data, multi-center validation, and bias reduction to improve model trust.

Explore integrating and deploying healthcare machine learning models through MLOps, covering data preparation, deployment, monitoring, and governance. Examine challenges like EMR integration, data quality, drift, and user acceptance.

Consolidating the final concepts, this course builds a foundation in ai and healthcare, guiding you to choose code or no-code and low-code paths and start real-data projects.