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Machine Learning for Software Engineers
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Before We Begin
About this Path
About this Module
Are You Ready to Become a Machine Learning Engineer?
Overview
Data Manipulation with NumPy
Introduction
NumPy Arrays
NumPy Basics
Math
Random
Indexing
Filtering
Statistics
Aggregation
Saving Data
Quiz
Data Analysis with pandas
Introduction
Series
DataFrame
Combining
Indexing
File I/O
Grouping
Features
Filtering
Sorting
Metrics
Plotting
To NumPy
Quiz
Data Preprocessing with scikit-learn
Introduction
Standardizing Data
Data Range
Robust Scaling
Normalizing Data
Data Imputation
PCA
Labeled Data
Quiz
Data Modeling with scikit-learn
Introduction
Linear Regression
Ridge Regression
LASSO Regression
Bayesian Regression
Logistic Regression
Decision Trees
Training and Testing
Cross-Validation
Applying CV to Decision Trees
Evaluating Models
Exhaustive Tuning
Quiz
Clustering with scikit-learn
Introduction
Cosine Similarity
Nearest Neighbors
K-Means Clustering
Hierarchical Clustering
Mean Shift Clustering
DBSCAN
Evaluating Clusters
Feature Clustering
Quiz
Gradient Boosting with XGBoost
Introduction
XGBoost Basics
Cross-Validation
Storing Boosters
XGBoost Classifier
XGBoost Regressor
Feature Importance
Hyperparameter Tuning
Model Persistence
Quiz
Deep Learning with TensorFlow
Introduction
Model Initialization
Logits
Metrics
Optimization
Training
Evaluation
Linear Limitations
Hidden Layer
Multiclass
Softmax
Quiz
Deep Learning with Keras
Introduction
Sequential Model
Model Output
Model Configuration
Model Execution
Quiz
Conclusion
Final Remarks
Cross-Validation
Use cross-validation to evaluate parameters for XGBoost.
We'll cover the following
Chapter Goals:
A. Choosing parameters
Chapter Goals:
Learn how to cross-validate parameters in XGBoost