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AI in Nutrition and Dietetics Course

Module 1: Foundations of AI in Nutrition

Chapter 1: AI/ML Basics for Health & Nutrition

Lesson 1.1 What is AI vs. ML vs. Deep Learning (health context)

Lesson 1.2 Supervised, Unsupervised & Reinforcement Learning

Lesson 1.3 Features, Labels, and Model Evaluation (accuracy, ROC-AUC, F1)

Lesson 1.4 — From Research to Product: AI lifecycle in nutrition apps

Chapter 2: Nutrition Science Primer & Digital Trends

Lesson 2.1 Macronutrients, Micronutrients, Dietary Patterns

Video Module 1: Foundations of AI in Nutrition

Video 1.1 – Introduction to AI and Machine Learning in Healthcare

Video 1.2 – Basics of Nutrition Science for AI-Enabled Applications

Video 1.3 – Digital Transformation in Nutrition and Dietetics

Video 1.4 – Food Databases, Nutritional Biomarkers, and Dietary Data

Video 1.5 – Tools and Platforms: MyFitnessPal, USDA FoodData Central, NutriSurvey, and Open APIs

Video Module 2: AI Applications in Diet Planning and Food Tracking

Video 2.1 – Personalized Meal Planning Using AI

Video 2.2 – Integrating Medical, Lifestyle, and Genetic Data in Nutrition Models

Video 2.3 – NLP in Food Logging and Dietary Recommendation Systems

Video 2.4 – Image Recognition for Food Tracking and Portion Analysis

Video 2.5 – Real-Time Nutrition Feedback Systems and Smart Coaching Assistants

Video 2.6 – Case Study: AI in Sports Nutrition

Video 2.7 – Case Study: AI in Pediatric Nutrition

Video 2.8 – Case Study: AI for Metabolic Disorders and Therapeutic Diets

Video Module 3: Predictive Analytics, Ethics, and Implementation

Video 3.1 – Predictive Analytics for Obesity, Diabetes, and Cardiovascular Risk

Video 3.2 – Deep Learning for Microbiome Analysis

Video 3.3 – AI in Nutrigenomics and Precision Nutrition

Video 3.4 – Ethics, Privacy, and Consent in AI-Led Health Personalization

Video 3.5 – Challenges in Clinical and Industry Implementation

Video 3.6 – Hands-on Mini Project: Building a Prototype Nutrition Recommendation Model with Dummy Health Data

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