Published 9/2025
Created by Khurshid Ayub
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 71 Lectures ( 4h 48m ) | Size: 2.23 GB
Python for Nutritional Analysis: Apply Data Science and Machine Learning to Explore Food and Diet Patterns
What you’ll learn
Gain hands-on experience with Python libraries (NumPy, Pandas, Matplotlib, Scikit-learn) for nutritional data analysis in health, food science, and diet plannin
Explore real-world datasets in nutrition, food composition, and dietary intake. Apply statistical and visualization techniques to uncover nutritional patterns
Develop and evaluate machine learning models for food classification, and diet optimization for health applications, research, and personal nutrition.
Confidently use Python as a tool to bridge the gap between nutrition science and data-driven insights for diet quality and nutrient balance
Apply supervised and unsupervised machine learning models (e.g., regression, classification, clustering) to nutritional problems.
Import, clean, and preprocess nutritional datasets using Python. Perform exploratory data analysis (EDA) to identify key nutritional features.
Requirements
Although i will try to cover essential basics of python required for this course but if you know basic python, that is an asset
Description
Nutrition today is more than just food science — it’s data science. With vast nutritional databases, dietary surveys, and food composition data available, the ability to analyze, visualize, and extract insights from nutrition data has become a powerful skill for researchers, dietitians, students, and professionals alike.This course brings the world of nutrition and Python together. Whether you’re a chemistry student, a nutrition researcher, or simply someone fascinated by how data can shape healthier choices, this course will give you the essential tools to analyze, plot, and even predict nutrition-related outcomes using Python.Instead of generic Python tutorials, you’ll learn Python directly through nutritional examples — making your learning highly relevant, engaging, and practical. From handling food datasets to visualizing macronutrients and applying machine learning for predictive analysis, each lesson is designed to connect programming skills with real-world nutrition applications.By the end of this course, you’ll be able to:Use Python to analyze and visualize food and nutrition datasets with confidenceUnderstand macronutrient and micronutrient patterns using data-driven methodsApply machine learning models to explore dietary predictions and classification problemsTransform raw food data into meaningful insights for research or personal projectsWhether you are a student, researcher, dietitian, or data enthusiast, this course will help you bridge the gap between nutrition and computational skills — preparing you for the growing demand for data-driven insights in nutrition and food science.Enroll today and take your first step into the future of nutritional analysis with Python and machine learning!
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