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Technical Analysis for Beginner
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ABOUT US
Technical Analysis for Beginner
Course Content
Total learning:
27 lessons
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Machine Learning for Investing 201
Orientation
0/4
1.1
Orientation
1.2
Workshop I : Linear Regression
1.3
Workshop II : Random Forest
1.4
Workshop III : False Signal Detection
Random Forest
0/6
2.1
Liner Regression
2.2
Random Forest Modeling
2.3
Random Forest Hyper-Parameter
2.4
Random Forest Gridsearch Hyper-Parameter
2.5
Random Forest Classifier
2.6
False Signal Detection
Imbalanced
0/10
3.1
Boosting Model
3.2
Imbalanced Basic
3.3
Undersample /Upsample imbalanced
3.4
Workshop : False Signal Detection with imbalanced
3.5
Smote Under Part I
3.6
Smote Under Part II
3.7
More Feature, Oversample vs Moreminority
3.8
Ada Boost
3.9
Workshop : Ada Boost
3.10
Light Gradient Boosting
XGBoost
0/7
4.1
What is XGBoost
4.2
XGBoost
4.3
Workshop : Predict SET Return by XGBoost Classifier
4.4
Solution : Predict SET Return by XGBoost Classifier
4.5
Parameter Optimization XGBoost
4.6
Workshop : XGBoost False Signal Detection
4.7
XGBoost Tips & Tricks
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