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AI Features

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

1.What you'll learn from this course

2.Data Manipulation with NumPy

Introduction NumPy Arrays NumPy Basics Math Random Indexing Filtering Statistics Aggregation Saving Data Quiz

3.Data Analysis with pandas

Introduction Series DataFrame Combining Indexing File I/O Grouping Features Filtering Sorting Metrics Plotting To NumPy Quiz

4.Data Preprocessing with scikit-learn

Introduction Standardizing Data Data Range Robust Scaling Normalizing Data Data Imputation PCA Labeled Data Quiz

5.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

6.Clustering with scikit-learn

Introduction Cosine Similarity Nearest Neighbors K-Means Clustering Hierarchical Clustering Mean Shift Clustering DBSCAN Evaluating Clusters Feature Clustering Quiz

7.Gradient Boosting with XGBoost

Introduction XGBoost Basics Cross-Validation Storing Boosters XGBoost Classifier XGBoost Regressor Feature Importance Hyperparameter Tuning Model Persistence Quiz

8.Deep Learning with TensorFlow

Introduction Model Initialization Logits Metrics Optimization Training Evaluation Linear Limitations Hidden Layer Multiclass Softmax Quiz

9.Deep Learning with Keras

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

Mock Interview

Machine Learning Fundamentals

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

Quiz

We'll cover the following...