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Learning from Data
Curriculum
4 Sections
16 Lessons
10 Weeks
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Chapter 17: Exploring Four Simple and Effective Algorithms
4
1.1
Guessing the Number: Linear Regression
10 mins
1.2
Moving to Logistic Regression
10 mins
1.3
Making Things as Simple as Naïve Bayes
10 mins
1.4
Learning Lazily with Nearest Neighbors
10 mins
Chapter 18: Performing Cross-Validation, Selection, and Optimization
4
2.1
Pondering the Problem of Fitting a Model
10 mins
2.2
Cross-Validating
10 mins
2.3
Selecting Variables Like a Pro
10 mins
2.4
Pumping Up Your Hyperparameters
10 mins
Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks
5
3.1
Using Nonlinear Transformations
10 mins
3.2
Regularizing Linear Models
10 mins
3.3
Fighting with Big Data Chunk by Chunk
10 mins
3.4
Understanding Support Vector Machines
10 mins
3.5
Playing with Neural Networks
10 mins
Chapter 20: Understanding the Power of the Many
3
4.1
Starting with a Plain Decision Tree
10 mins
4.2
Getting Lost in a Random Forest
10 mins
4.3
Boosting Predictions
10 mins
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