📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Foundations for Students and Beginners, from foundations to a certified capstone project.
Outline
Intro to AI, its branches (ML, Deep Learning, NLP) • How AI is used in industries • Key concepts: supervised vs unsupervised learning, classification, regression • Simple AI models using Python and Scikit-learn
Outline
Learn Python basics for AI programming • Setting up Python and Jupyter Notebooks • Python basics: data types, loops, functions • Introduction to NumPy and Pandas for data manipulation
Outline
Understanding NLP and how machines process language • Working with text data and preprocessing • Basic NLP models: tokenization, stemming, lemmatization • Using libraries like NLTK and spaCy
Outline
Introduction to Scikit-learn • Building models like Decision Trees, K-Nearest Neighbors, and Linear Regression • Measuring performance with accuracy, precision, recall • Project: Predict student performance based on previous data
Earn government-registered certification in AI Foundations for Students and Beginners
e-Certificate and e-Marksheet issued on successful completion.
