Machine learning is reshaping software, science, and business. This course demystifies what ML actually is, how the major algorithms work, and the realistic workflow used by practitioners. You leave able to talk fluently with ML engineers and apply core techniques yourself.
We focus on intuition first, then enough math and code to make the ideas stick. Every algorithm is illustrated with a small Python example you can run on your own machine using the standard scikit-learn toolkit.
What you’ll learn
- Distinctions between supervised, unsupervised, and reinforcement learning
- Core algorithms: linear regression, logistic regression, decision trees, random forests, neural networks
- The full ML workflow: data preparation, training, evaluation, deployment
- Feature engineering and dealing with messy real-world data
- How to measure model performance and avoid overfitting
Who is this for
Designed for software engineers, analysts, and product people who want a working understanding of ML without spending a year on a graduate program. Existing ML practitioners can also use this as a refresher of core concepts. Some Python helps but isn’t mandatory if you’re willing to read along.
Prerequisites
- Basic familiarity with Python (or another scripting language)
- High-school level algebra and statistics
- A computer that can run Python with scikit-learn (any modern laptop works)
Course outcomes
You will be able to frame business problems as ML problems, choose appropriate algorithms, prepare data effectively, and evaluate models honestly. This is the foundation needed to go deeper into specialized topics like deep learning, NLP, or MLOps.