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IT: Artificial Intelligence & Data
Model Selection and Advanced Methods
Curriculum
1 Section
24 Lessons
30 hours
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Unit 3 — Model Selection and Advanced Methods
24
1.1
Training, Validating, and Testing
20 mins
1.2
Taking a Probabilistic Turn
20 mins
1.3
Tuning the K-Means Algorithm
20 mins
1.4
Switching to Probabilities
20 mins
1.5
Classifying and Estimating with SVM
20 mins
1.6
Introducing Deep Learning
20 mins
1.7
Boosting via Gradient Descent
20 mins
1.8
Classifying Images with CNNs
20 mins
1.9
Understanding How Machines Read
20 mins
1.10
Leveraging SVD
20 mins
1.11
Choosing the Right Error or Score Metric
20 mins
1.12
Avoiding Inference Pitfalls
20 mins
1.13
Considering the Relationship Between AI and Machine Learning
20 mins
1.14
Understanding the Role of Algorithms
20 mins
1.15
Performing Common Tasks
20 mins
1.16
Performing Multimedia Integration
20 mins
1.17
Understanding the Basics of Classes
20 mins
1.18
Optimizing by Cross-Validation
20 mins
1.19
Finding Similarity by K-Nearest Neighbors
20 mins
1.20
Guessing the Right Features
20 mins
1.21
Averaging Different Predictors
20 mins
1.22
Using Scoring and Classification
20 mins
1.23
Searching for the Best Hyperparameters
20 mins
1.24
Using Generalizations Correctly
20 mins
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