Introduction to Supervised Machine Learning
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.
Lessons and practices
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
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
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
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
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
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
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
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