Learn how machines learn from data, starting with your first mean calculation and progressing to building regression models and decision trees that can actually predict the future.
Learn how machines learn from data, starting with your first mean calculation and progressing to building regression models and decision trees that can actually predict the future.
Machine learning sounds intimidating until someone shows you that it is just pattern recognition with better tools. This course teaches ML the way it actually clicks, through real-world analogies, bite-sized Python examples, and the statistical intuition that separates someone who copies code from s...
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Understand what machine learning actually is, what data sets look like to a computer, and the three data types that determine every analysis technique you will ever use.
Learn the three most fundamental ways to summarise a data set with a single number — and when each one lies to you.
Discover how spread out your data really is using standard deviation, variance, and percentiles — the metrics that reveal what averages hide.
Generate large data sets, understand uniform and normal distributions, and use histograms and scatter plots to spot patterns the numbers alone cannot show.
Fit a straight line through your data, measure how well it fits with the r-value, and make your first real predictions.
Handle curved relationships with polynomial regression, predict outcomes from multiple variables, and scale features so your model treats every input fairly.
Split your data to honestly evaluate your model, build decision trees that make human-readable predictions, and tie the entire ML workflow together.
