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    • Machine Learning for Business Intelligence Part 2 Classification Modeling

    Machine Learning for BI, PART 2: Classification Modeling

    BY
    Udemy

    Brochure Enquire
    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 449 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Getting Started

    • Course Structure & Outline
    • READ ME: Important Notes for New Students
    • About this Series
    • DOWNLOAD: Course Resources
    • Setting Expectations

    Intro to Classification

    • Supervised vs. Unsupervised Learning
    • Classification vs. Regression
    • RECAP: Key Concepts
    • Classification 101
    • Classification Workflow
    • Feature Engineering
    • Data Splitting
    • Overfitting
    • Intro to Classification

    Classification Models

    • Common Classification Models
    • Intro to K-Nearest Neighbors (KNN)
    • KNN Examples
    • CASE STUDY: KNN
    • Intro to Naïve Bayes
    • Naïve Bayes | Frequency Tables
    • Naïve Bayes | Conditional Probability
    • CASE STUDY: Naïve Bayes
    • Intro to Decision Trees
    • Decision Trees | Entropy 101
    • Entropy & Information Gain
    • Decision Tree Examples
    • Random Forests
    • CASE STUDY: Decision Trees
    • Intro to Logistic Regression
    • Logistic Regression Example
    • False Positives vs. False Negatives
    • Logistic Regression Equation
    • The Likelihood Function
    • Multivariate Logistic Regression
    • CASE STUDY: Logistic Regression
    • Intro to Sentiment Analysis
    • Cleaning Text Data
    • "Bag of Words" Analysis
    • CASE STUDY: Sentiment Analysis
    • Classification Models

    Model Selection & Tuning

    • Intro to Selection & Tuning
    • Hyperparameters
    • Imbalanced Classes
    • Confusion Matrix
    • Accuracy, Precision & Recall
    • Multi-class Confusion Matrix
    • Multi-class Scoring
    • Model Selection
    • Model Drift
    • Model Selection & Tuning

    Wrapping Up

    • Looking Ahead to Part 3
    • BONUS LECTURE: More from Maven

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