1 - Python Machine Learning Toolkit
Supervised Machine LearningJupyter NotebookspandasData Quality Considerations
2 - Exploratory Data Analysis and Visualization
Summary Statistics and Central ValuesMissing ValuesDistribution of ValuesRelationships within the Data
3 - Regression Analysis
Regression and Classification ProblemsLinear RegressionMultiple Linear RegressionAutoregression Models
4 - Classification
Linear Regression as a ClassifierLogistic RegressionClassification Using K-Nearest NeighborsClassification Using Decision Trees
5 - Ensemble Modeling
Overfitting and UnderfittingBaggingBoosting
6 - Model Evaluation
Evaluation MetricsSplitting the DatasetPerformance Improvement Tactics
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.