• 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