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- Course Structure & Outline
- READ ME: Important Notes for New Students
- About this Series
- DOWNLOAD: Course Resources
- Setting Expectations
Machine Learning for BI, PART 2: Classification Modeling
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 449 ₹3,499
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Getting Started
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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