Intermediate · 7 lessons · 220 min
Data Science
Machine Learning Foundations
Theory meets practice - build, train, and evaluate real models
ml
scikit-learn
regression
classification
python
Taught by Diego Park·1 learner enrolled
About this course
A rigorous but accessible introduction to supervised machine learning. We start with linear regression, move to classification with logistic regression and decision trees, and finish with a short introduction to neural networks. Every concept is paired with hands-on scikit-learn code and an adaptive AI tutor.
Curriculum
4 modules01 The ML Mindset 2 lessons · 55 min
1
Supervised vs unsupervised, train/test split
25 min · easy
2
Loss functions and the bias-variance tradeoff
30 min · medium
02 Linear Models 2 lessons · 70 min
1
Linear regression from scratch
35 min · medium
2
Logistic regression & ROC curves
35 min · hard
03 Trees & Ensembles 2 lessons · 65 min
1
Decision trees & Gini impurity
30 min · medium
2
Random forests & gradient boosting
35 min · hard
04 A Peek Into Neural Networks 1 lesson · 30 min
1
Perceptrons and backprop intuition
30 min · hard
$49
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