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Course Introduction
About This Course
Linear Regression
Introduction to Linear Regression
Welcome and Data Overview
Exploratory Data Analysis
Variance and Covariance
Machine Learning
Model Evaluation
Model Explainability
Finalizing and Serializing the Model
Model Deployment in the Notebook
Cross-validation
Summary
Exercise: Model the Boston Housing Dataset
Solution: Model the Boston Housing Dataset
Exercise: Model the King County's House Sales Dataset
Solution: Model the King County's House Sales Dataset
Quiz: Linear Regression
Regularization
Introduction to Regularization
Penalties
Effect of Regularization
Model Performance
Summary
Quiz: Regularization
Bias-Variance Trade-off
Introduction and Data Generation
Morale Function and Model Error
Generating Student Samples and Plot
Model Building to Predict the Morale
Bias-Variance Trade-off
Summary
Quiz: Bias Variance Trade-off
Categorical Features
Quantitative vs. Qualitative Data and Creating Dummies
Redundant Variables and Machine Learning
Summary
Exercise: Model the Auto MPG Dataset
Solution: Model the Auto MPG Dataset
Quiz: Categorical Features
Logistic Regression
Introduction to Logistic Regression
Probability and Odds
Understanding Logistic Regression
Logistic Regression Implementation
Model Evaluation
Summary
Quiz: Logistic Regression
Logistic Regression: Titanic Data
The Dataset and Exploratory Data Analysis
Data Preprocessing
Getting Data Ready and Building Machine Learning Model
Predictions and Evaluation
Model Evaluation
Feature Importance and Model Explainability
Summary
Exercise: Retrain the Model Without Pclass Dummies
Solution: Retrain the Model Without Pclass Dummies
Quiz: Logistic Regression—Titanic Data
Project
Sentiment Analysis Using Multinomial Logistic Regression
Multiclass Classification and Handling Imbalanced Classes
Introduction to Multiclass Logistic Regression Classification
Imbalanced Datasets and Techniques to Handle Them
Machine Learning and Imbalanced Data
Performance of the Trained Models on Unseen Data
Summary
Quiz: Multiclass Classification and Handling Imbalanced Classes
Project: Predicting Chronic Kidney Disease
Problem Definition and the Data
Exploratory Data Analysis
Data Preprocessing
Model Training and Evaluation
K-Nearest Neighbors
Introduction to K-Nearest Neighbors
Working Principle of K-Nearest Neighbors
Visualize the Working of K-Nearest Neighbors
Final Thoughts
Quiz: K-Nearest Neighbors
Implementation of K-Nearest Neighbors
The Dataset and Exploratory Data Analysis
Model Training Using Unscaled Data
Effect of Feature Scaling
Model Training Using Scaled Features
Elbow Method to Chose the K Value
Model Comparisons
Quiz: Implementation of K-Nearest Neighbors
Logistic Regression vs. KNN
The Dataset and Exploratory Data Analysis
Baseline Accuracy and Machine Learning Model Selection
Exercise: Find the Best Combination of Parameters
Solution: Find the Best Combination of Parameters
Exercise: Find the Best Value for K
Solution: Find the Best Value for K
Decision Tree Learning
Introduction to Decision Trees
Bootstrap, Bagging, and Random Forests
The Dataset and Exploratory Data Analysis
Machine Learning
Feature Importance
Hyperparameters and Their Tuning
ROC Curve
Playing With Probability Cut-off
Tree Visualization
Exercise: Train a Decision Tree Model Using the Gini Criteria
Solution: Train a Decision Tree Model Using the Gini Criteria
Exercise: Train a Random Forest Model Using the Entropy Criteria
Solution: Train a Random Forest Model Using the Entropy Criteria
Quiz: Decision Tree Learning
Project
Implement the Decision Tree Classifier from Scratch
Bootstrapping and Confidence Interval
Introduction to Bootstrapping and Confidence Interval
The Data
Confidence Interval of Mean
Confidence Interval for the Median
Quiz: Bootstrapping and Confidence Interval
Support Vector Machine
Introduction to Support Vector Machine
Visualize the Working of the Support Vector Machine
The Dataset and Exploratory Data Analysis
Feature Selection
Machine Learning
Grid Search
Feature Scaling
ROC Curve and Saving the Model
Quiz: Support Vector Machine
Practice and Comparisons
MNIST: Handwritten Digits Dataset
The Iris Dataset
The Circles Dataset
What's Next?
Final Thoughts and Future Directions
Appendix
R-square and Goodness of the Fit
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