An overview course into supervised machine learning techniques, focusing particularly on linear and logistic regression. By working with real-world datasets, you will implement both models to predict outputs and analyze the most predictive features.
Overview
Syllabus
- Lesson 1: Diving into the Wine Quality Dataset: An In-depth Overview
- Discovering the Wine Quality Dataset
- Explore and Debug the Martian Wine Festival Data
- Adding the Quality Distribution Histogram to the Red Wine Exploration
- Examining Quality Distribution and Missing Values in Red Wine Dataset
- Lesson 2: Mastering Gradient Descent: Your Guide to Optimizing Machine Learning Models
- Visualizing Gradient Descent using Wine Density
- Experimenting with Different Learning Rates
- Understanding Model Performance: Debugging Gradient Descent on Test Dataset
- Implementing Gradient Descent for Wine Quality Prediction Based on 'Chlorides' Feature
- Implementing Gradient Descent on 'Sulphates' Feature in Wine Quality Dataset
- Lesson 3: Mastering Linear Regression: From Theories to Predictions
- Analyzing Wine Quality Based on Fixed and Volatile Acidity using Linear Regression
- Examining the Density Effect on Wine Quality
- Unraveling the Correlation in Red Wine with Linear Regression
- Analyzing the Effect of Volatile Acidity and Sulfur Dioxide on Wine Quality with Linear Regression
- Analyzing the Effect of Fixed Acidity and pH on White Wine Quality with Linear Regression
- Lesson 4: Unveiling Logistic Regression: Internals, Design, and Hands-On Implementation with Wine Quality Prediction
- Modeling Wine Quality Predictions with Logistic Regression
- Changing the Wine Quality Rating Threshold
- Identify and Fix the Logistic Regression Model Issue
- Train the Model and Make Predictions
- Training and Evaluating a Binary Logistic Regression Model from Scratch
- Lesson 5: Assessing Model Accuracy: Comprehensive Evaluation Metrics and Techniques in Machine Learning
- Evaluating a House Price Prediction Model with Regression Metrics
- Evaluating Excellent Wines with Logistic Regression
- Evaluating Logistic Regression Model with California Housing Dataset
- Evaluating Logistic Regression with Different Testing Sizes
- Lesson 6: Unveiling Predictive Features: A Close Look at Wine Quality with Correlation Analysis
- Exploring the Correlation between 'pH' and 'Fixed Acidity' in Wine Quality Dataset
- Analyzing the Correlation between Density and Quality
- Exploring Correlations in the Wine Quality Dataset
- Visualizing Correlations in the Wine Quality Dataset as a Heatmap
- Finding Fixed Acidity's Influence on Wine Quality
- Lesson 7: Unraveling Model Improvement and Fine-Tuning in Machine Learning
- Enhancing Wine Quality Prediction with Hyperparameter Fine-Tuning
- Exploring Low Quality Wine Prediction
- Tuning Hyperparameters for the Wine Predictor Model
- Hyperparameter Tuning for Wine Quality Prediction Model
- Writing the Wine Predictor Model with L1 and L2 Regularization
