Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

CodeSignal

Training Your First Machine Learning Model from Scratch

via CodeSignal

Go to class Write review

Overview

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.

Syllabus

  • Unit 1: Introduction to Machine Learning
    • House Area and Price Relationship
    • Enhance Data Visualization by Changing Sample Size
    • Complete the Data Generation and Visualization
  • Unit 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
  • Unit 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
  • Unit 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)
  • Unit 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

Reviews

Start your review of Training Your First Machine Learning Model from Scratch

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.