AI Features

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All LessonsFree Lessons (13)

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 Pandas Cheat Sheet 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

Linear Regression

Ridge Regression

LASSO Regression

Bayesian Regression

Logistic Regression

Training and Testing

Cross-Validation

Applying CV to Decision Trees

Evaluating Models

Exhaustive Tuning

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

Sequential Model

Model Configuration

Model Execution

Course Conclusion

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

Introduction

An overview of the main models used in scikit-learn.

We'll cover the following...

A. Creating models for data

In this chapter, you will continue to use scikit-learn, ...