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All LessonsFree Lessons (12)

What you'll learn from this course

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

Model Initialization

Linear Limitations

Deep Learning with Keras

Introduction Sequential Model Model Output Model Configuration Model Execution Quiz Course Conclusion

Machine Learning with NumPy, pandas, scikit-learn, and More/

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Quiz

Quiz

We'll cover the following...

Technical Quiz

1.

What is a benefit to using more hidden layers in a neural network?

A.

Additional hidden layers allow the model to learn more complex decision boundaries

B.

Using more hidden layers will speed up model training

C.

A neural network with only a single hidden layer is more computationally expensive

D.

Using more hidden layers helps regularize the neural network

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