Training Your First Machine Learning Model from Scratch
This course delves into the foundational steps required to build and train a linear regression model from scratch using scikit-learn. You will understand the basics of model training, evaluation, and prediction.
Lessons and practices
Lesson 1: Introduction to Machine Learning
House Area and Price Relationship
Enhance Data Visualization by Changing Sample Size
Complete the Data Generation and Visualization
Lesson 2: Training a Linear Regression Model
Training the Linear Regression Model
Adjust Base Price and Price per Square Foot
Predicting House Prices with Linear Regression
Training a Linear Regression Model from Scratch
Train Your Linear Regression Model
Lesson 3: Making Predictions and Visualizing Results
Predicting and Visualizing House Prices
Visualize Predicted Prices Using Line Plot
Predict House Prices
Debugging House Price Predictions
Making Predictions from Scratch
Lesson 4: Evaluating Your Model's Performance
Evaluating House Price Predictions
Removing Noise from Synthetic Data
Predict House Prices and Calculate MSE
Calculating and Comparing Mean Squared Error (MSE)
Lesson 5: Applying Linear Regression to the Real Dataset
Predicting California House Prices
Modifying Feature Selection to Evaluate MSE
California Housing Model Debugging
Preparing Data and Making Predictions
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.