Healthcare Artificial Intelligence
Create powerful AI models for real-world healthcare applications using Data Science, Machine Learning, and Deep Learning.
Course Overview
The Complete Healthcare Artificial Intelligence Course empowers learners to design, build, and deploy AI-driven healthcare solutions using modern data science and machine learning tools. Whether you are a beginner or an experienced professional looking to transition into healthcare AI, this course provides a hands-on, end-to-end understanding of how intelligent systems can transform patient outcomes, diagnostics, and medical research.
You’ll learn the core mathematical foundations, coding techniques, and algorithmic design patterns behind state-of-the-art AI systems—while building practical models that address real healthcare challenges such as disease prediction, diagnosis support, and treatment optimization.
What You’ll Learn
Core Python and Data Science Tools: Pandas, Seaborn, Matplotlib, and Anaconda for data analysis and visualization.
Deep Learning and Neural Networks: Artificial Neural Networks (ANNs), Keras, Google Colab, Jupyter Notebook, and Deep Feedforward Networks.
Activation Functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Exponential Linear Unit (ELU), and Swish functions.
Machine Learning Algorithms: Logistic Regression, Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, and Markov Models.
Advanced Model Techniques: Stacking models, Maximum Voting Classifiers, Response Encoding, and One-Hot Encoding.
Data Preprocessing: Data cleaning, handling missing values, normalization, feature scaling, temporal and geolocation feature extraction, and data standardization.
Exploratory Data Analysis: Data visualization, geolocation mapping, anomaly detection, and correlation studies.
Model Evaluation: Confusion Matrix, ROC Curve, and accuracy testing.
Natural Language Processing (NLP): Using NLTK (Natural Language Toolkit) for healthcare text and response data analysis.
AI in Healthcare Applications: Building classification models for medical diagnostics, disease prediction, and healthcare optimization.
Hands-on Healthcare Projects
You’ll apply what you learn through real-world, project-based learning, building your own models and solutions for actual healthcare problems.
Projects include:
DNA Classification Project
Heart Disease Classification Project
Coronary Artery Disease Diagnosis
Breast Cancer Detection
Diabetes Prediction with Multilayer Perceptrons
Predicting Taxi Fares in New York City
Iris Flower Classification
Medical Treatment Recommendation Project
Who This Course Is For
This course is designed for:
Beginners and students with a basic understanding of Python or mathematics who want to start learning AI, ML, and DL.
Intermediate learners who already understand classical algorithms (e.g., linear regression, logistic regression) and want to go deeper into applied AI.
Data analysts and engineers who want to transition into Data Science or AI for healthcare.
Healthcare professionals seeking to apply AI and data analytics in clinical or operational settings.
Business professionals looking to create added value through AI-driven healthcare innovation.
Anyone eager to build a career as a Data Scientist, Machine Learning Engineer, or AI Specialist in the healthcare industry.
Requirements
No formal prerequisites.
Basic familiarity with Python programming is helpful—but everything is taught from the ground up.
Course Highlights
Comprehensive coverage of Machine Learning, Deep Learning, and Artificial Intelligence concepts.
Step-by-step tutorials designed by an experienced software engineer.
Hands-on projects replicating real-world healthcare applications.
Practical skills to analyze, visualize, and model complex medical data.
Build deployable AI models that improve accuracy and decision-making in healthcare.
Key Outcomes
By the end of this course, you will be able to:
Import, preprocess, and visualize healthcare datasets.
Build and compare machine learning and deep learning classification models.
Handle missing data and anomalies effectively.
Train, validate, and test models for predictive healthcare analytics.
Implement Natural Language Processing for medical text data.
Deploy AI models that achieve high accuracy and practical clinical relevance.
Compare and contrast classification machine learning techniques. Build AI models with precision and confidence. Become part of the next generation of healthcare innovators using Artificial Intelligence.