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Machine Learning A-Z
Curriculum
4 Sections
16 Lessons
4 Hours
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ML Foundations
4
1.1
What Machine Learning Is and Is Not
8 Minutes
1.2
Supervised vs Unsupervised Learning
8 Minutes
1.3
The Train-Test Mindset
8 Minutes
1.4
Setting Up Your ML Environment
20 Minutes
Supervised Learning
4
2.1
Linear and Logistic Regression
8 Minutes
2.2
Decision Trees and Random Forests
20 Minutes
2.3
Model Evaluation Metrics
19 Minutes
2.4
Overfitting and Regularisation
8 Minutes
Unsupervised and Beyond
4
3.1
Clustering with K-Means
20 Minutes
3.2
Dimensionality Reduction (PCA)
8 Minutes
3.3
Feature Engineering That Wins
22 Minutes
3.4
Ensembles and Boosting
15 Minutes
ML in Practice
4
4.1
The End-to-End ML Pipeline
20 Minutes
4.2
Hyperparameter Tuning
13 Minutes
4.3
Deploying a Simple Model
21 Minutes
4.4
Course Project: Predictive Model Build
14 Minutes
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