ML for Software Engineers: A 5-Day Primer

A concise course designed to equip software engineers with practical machine learning skills in just five days.

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Introduction to Machine Learning Fundamentals

Unit 1: What is Machine Learning?

Unit 2: Reinforcement Learning and the ML Workflow

Unit 3: Training, Evaluation, and Python Libraries

Supervised Learning: Regression and Classification

Unit 1: Linear Regression Fundamentals

Unit 2: Logistic Regression for Classification

Unit 3: Decision Trees and Random Forests

Data Preprocessing and Feature Engineering

Unit 1: Handling Missing Data

Unit 2: Scaling Numerical Features

Unit 3: Encoding Categorical Variables

Unit 4: Feature Engineering

Model Evaluation and Selection

Unit 1: Cross-Validation Techniques

Unit 2: Bias-Variance Tradeoff

Unit 3: Evaluation Metrics

Unit 4: Model Selection

Regularization and Overfitting

Unit 1: Understanding Overfitting

Unit 2: L1 and L2 Regularization

Unit 3: Hyperparameter Tuning and Model Evaluation

Unsupervised Learning: Clustering and Dimensionality Reduction

Unit 1: Introduction to Unsupervised Learning

Unit 2: K-Means Clustering

Unit 3: Dimensionality Reduction with PCA

Unit 4: Evaluating Unsupervised Learning Models

Unit 5: Advanced Unsupervised Techniques

Model Deployment and Monitoring

Unit 1: Preparing Models for Deployment

Unit 2: Deploying Models with Flask

Unit 3: Model Versioning and A/B Testing