Learning Objectives

• Pre-process data to make it ready to use for machine learning
• Create data visualizations with Matplotlib
• Use scikit-learn to perform dimension reduction using principal component analysis (PCA)
• Solve classification and regression problems
• Get predictions using the XGBoost library
• Process images and create machine learning models to decode them
• Process human language for prediction and classification
• Use TensorBoard to monitor training metrics in real time
• Find the best hyperparameters for your model with AutoML