Advanced Machine Learning

In the Advanced Machine Learning training course, you'll learn:

Brief Review of ML Concepts

Advanced Feature Engineering

• Dealing with Dirty Data
• Feature Value Imputation
• Engineered Features
• Denoising With ML Models

Optimizing and Training Linear Models

• Understanding Gradient Descent Optimization
• L1 and L2 / Elasticnet Optimization
• Support Vector Machines
• k-Folds Cross Validation

Evaluating Models

• Metrics for Model Evaluation
• How to Optimize and Tune Hyperparameters
• Adjusting Thresholds to Tune for Positive or Negative Performance
• Adjusting for Class Imbalance
• Advanced Metrics for Model Evaluation

Handling Outliers

• Dealing with Outliers
• Detecting Outliers C
• Partitioning Models to Handle Outliers

Recommendations

• Exploring Similarity Metrics
• User-based vs. Item-based Recommendations
• Evaluating Recommendation Results
• Combining Multiple Models for Recommendations

Ensemble Methods

• Using Multiple Models Together
• Random Forests and GBTs
• Understanding Bootstrap Aggregation
• Combining Heterogenous Models
• Evaluating Ensembles of Methods

Time Series Data

• Inferring State from Time-Series Data
• Sequence Prediction
• ARIMA
• Using RNNs for Time-Series Data