// Version: v5
const courseTopics = [
    { category: "Python", topics: [
        { name: "Python - M1W1 - Intro to Python", amount: 3000 },
        { name: "Python - M1W2 - Data Manipulation", amount: 3000 },
        { name: "Python - M1W3 - Exploratory Data Analysis", amount: 3000 },
        { name: "Python - M1W4 - Analyzing Text Data", amount: 3000 }
    ]},
    { category: "Machine Learning", topics: [
        { name: "Machine Learning - M2W1 - Linear Regression", amount: 3000 },
        { name: "Machine Learning - M2W2 - Decision Trees", amount: 3000 },
        { name: "Machine Learning - M2W3 - K-Means Clustering", amount: 3000 }
    ]},
    { category: "Advanced Machine Learning", topics: [
        { name: "Advanced Machine Learning - M3W1 - Bagging", amount: 3000 },
        { name: "Advanced Machine Learning - M3W2 - Boosting", amount: 3000 },
        { name: "Advanced Machine Learning - M3W3 - Model Tuning", amount: 3000 }
    ]},
    { category: "Neural Networks", topics: [
        { name: "Neural Networks - M4W1 - Intro to Neural Networks", amount: 4000 },
        { name: "Neural Networks - M4W2 - Optimizing Neural Networks", amount: 4000 }
    ]},
    { category: "NLP with GenAI", topics: [
        { name: "NLP with GenAI - M5W1 - Word Embeddings", amount: 4000 },
        { name: "NLP with GenAI - M5W2 - Attention Mechanism and Transformers", amount: 4000 },
        { name: "NLP with GenAI - M5W3 - LLM and Prompt Engineering", amount: 4000 },
        { name: "NLP with GenAI - M5W4 - RAG - Retrieval Augmented Generation", amount: 4000 }
    ]},
    { category: "Computer Vision (CV)", topics: [
        { name: "Computer Vision - M6W1 - Image Processing", amount: 4000 },
        { name: "Computer Vision - M6W2 - CNN (Convolutional Neural Networks)", amount: 4000 }
    ]},
    { category: "Model Deployment", topics: [
        { name: "Model Deployment - M7W1 - Intro to Model Deployment", amount: 3000 },
        { name: "Model Deployment - M7W2 - Containerization", amount: 3000 }
    ]},
    { category: "SQL (Structured Query Language)", topics: [
        { name: "SQL - M8W1 - Querying Data with SQL", amount: 3000 },
        { name: "SQL - M8W2 - Advanced Querying", amount: 3000 },
        { name: "SQL - M8W3 - Enhancing Query Proficiency", amount: 3000 }
    ]},
    { category: "Statistics", topics: [
        { name: "Statistics - M9W1 - Inferential Statistics Foundations", amount: 3000 },
        { name: "Statistics - M9W2 - Estimation and Hypothesis Testing", amount: 3000 },
        { name: "Statistics - M9W3 - Common Statistical Tests", amount: 3000 }
    ]},
    { category: "MLOps", topics: [
        { name: "MLOps - M10W1 - Model Interpretability", amount: 3000 },
        { name: "MLOps - M10W2 - Introduction to DevOps and MLOps", amount: 3000 },
        { name: "MLOps - M10W3 - Building ML Pipelines", amount: 3000 }
    ]},
    { category: "Advanced GenAI for NLP", topics: [
        { name: "Advanced GenAI for NLP - M11W1 - AI Assistant Development", amount: 4000 },
        { name: "Advanced GenAI for NLP - M11W2 - Fine-tuning LLMs", amount: 4000 }
    ]},
        ];