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Revisiting Machine Learning Fundamentals

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Overview

Work through a practical, end-to-end machine learning project: explore and visualize data, apply preprocessing, build and evaluate models, and deploy a simple REST API. This course refreshes your core ML skills and ensures you’re ready for the more complex, cloud-based workflows ahead.

Syllabus

Unit 1: Getting to Know Your Data

Loading Your Dataset
Examining Dataset Structure and Schema
Assessing Data Quality and Completeness
Visualizing Target Variable Distribution
Feature Correlation Heatmap Visualization

Unit 2: Preparing Data for Machine Learning Models

Engineering Meaningful Features from Existing Data
Splitting Data for Proper Model Validation
Calculating Outlier Thresholds from Training Data
Applying Outlier Caps to Both Datasets
Preserving Your Preprocessed Data

Unit 3: Training a Machine Learning Model

Separating Features from Target Variables
Training a Linear Regression Model
Making Predictions with Your Trained Model
Evaluating Model Performance Metrics
Saving Your Trained Model

Unit 4: Evaluating Trained Model Performance

Loading Models and Making Predictions
Calculating Model Performance with MSE
Computing All Essential Evaluation Metrics
Visualizing Model Performance with Scatter Plots

Unit 5: Deploying Models as REST APIs

Creating a REST API
Building a Prediction Endpoint
Connecting Real Models to APIs
Adding Robust Error Handling

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