Fundamentals of Machine Learning for Software Engineers Course Syllabus

Overview: This course provides a hands-on introduction to machine learning tailored specifically for software engineers. Over approximately 6 hours, you'll progress from foundational concepts to building and deploying models from scratch. Each module emphasizes code implementation, ensuring you gain practical experience without relying on black-box libraries. By the end, you'll have a working understanding of core ML systems and the ability to integrate them into real software workflows.

Module 1: How Machine Learning Works

Estimated time: 0.5 hours

• Introduction to ML paradigms
• Supervised vs unsupervised learning
• Basic neural networks
• Comparing traditional code to ML-based behavior programming

Module 2: Our First Learning Program (Linear Regression)

Estimated time: 1 hour

• Designing a linear regression model
• Understanding the bias term
• Adjusting learning rates
• Building, training, and testing on real data

Module 3: Walking the Gradient (Gradient Descent)

Estimated time: 0.75 hours

• Understanding gradient descent
• Parameter optimization techniques
• Convergence behavior analysis
• Manual implementation and tuning of learning rates
• Visualizing training progress

Module 4: Neural Networks

Estimated time: 1.5 hours

• Components of an artificial neuron
• Activation functions
• Forward and backward pass mechanics
• Coding a neural network from scratch
• Training on sample datasets

Module 5: Deep Learning (Layered Nets)

Estimated time: 1.5 hours

• Multi-layer neural networks
• Backpropagation algorithm
• Basic deep learning design principles
• Extending neural nets with additional layers
• Training on more complex datasets

Module 6: Putting It All Together

Estimated time: 1 hour

• Integrating full ML pipelines
• Model versioning strategies
• Real-world deployment considerations
• End-to-end data processing and model deployment project

Prerequisites

• Basic programming experience in Python
• Familiarity with fundamental software engineering concepts
• Understanding of basic mathematical operations and functions

What You'll Be Able to Do After

• Build and train machine learning models from scratch
• Implement gradient descent and optimize model parameters
• Design and code neural networks without relying on high-level APIs
• Preprocess and engineer data for robust ML pipelines
• Deploy trained models and integrate them into software systems