Machine learning curriculum concept illustration

Course Curriculum

Explore the 7-day step-by-step learning path to master machine learning for engineering applications

Start For Free

Learning Journey Overview

Each module is structured to maximize your understanding through concise lessons, hands-on exercises, and real engineering examples. No prior ML experience needed.

Engineer studying with machine learning books and laptop
  • 7 focused modules delivered over 7 days
  • Theory and practice, always tied to engineering
  • Engineering datasets and real-world problem sets
  • Step-by-step code tutorials (Python with ready notebooks)
  • Mini-project to apply everything you learn

Detailed Curriculum

Day 1: Fundamentals of Machine Learning for Engineers

Topics Covered:
- What is Machine Learning?
- Types of ML tasks (Regression, Classification, Clustering)
- How ML fits into engineering workflows
Hands-on: Simple ML prediction demo with engineering data.

Day 2: Preparing Your Engineering Data

Topics Covered:
- Data cleaning and preprocessing
- Feature engineering for sensors, signals, and time series
- Handling missing/outlier values in engineering contexts
Hands-on: Guided data preparation with real sensor dataset.

Day 3: Regression Models in Action

Topics Covered:
- Linear vs. polynomial regression
- Applications: Predicting outputs (e.g., temperature, vibration)
- Interpreting model results in engineering terms
Hands-on: Build, train, and evaluate regression models.

Day 4: Classification for Fault Detection & Quality Control

Topics Covered:
- Classification algorithms overview (Logistic Regression, k-NN, Decision Trees)
- Binary vs. multiclass cases
- Engineering use-cases: Fault detection, pass/fail systems
Hands-on: Implement classification for anomaly or defect detection.

Day 5: Unsupervised Learning for Pattern Discovery

Topics Covered:
- Clustering basics (K-Means)
- Dimensionality reduction (PCA)
- Engineering scenarios: Grouping system behaviors, process analysis
Hands-on: Cluster and visualize patterns in engineering datasets.

Day 6: Model Evaluation and Validation

Topics Covered:
- Performance metrics for regression and classification
- Cross-validation and overfitting
- Translating metrics to engineering impact
Hands-on: Evaluate model performance with real engineering KPIs.

Day 7: Capstone Mini-Project

Topics Covered:
- Framing a realistic engineering problem
- Data-to-model workflow from scratch
Hands-on: Complete a project: From raw data to real ML results, with instructor guidance and template notebooks.

How You'll Learn

Online video lessons illustration
Bite-Sized Video Lessons

Clear, concise explainer videos focused on core concepts and their applications in engineering.

Interactive coding notebook visual
Interactive Notebooks

Ready-to-run Python notebooks to experiment with data and practice each ML concept step by step.

Engineering project and teamwork
Applied Mini-Project

Tackle a real-world engineering use-case using the full ML workflow, and prepare a sharable project portfolio piece.

Ready to Start Your Learning Journey?

All resources, code, and datasets included. No prior machine learning experience required!

Enroll Now