1 - Data Exploration and Cleaning
Python and the Anaconda Package Management SystemDifferent Types of Data Science ProblemsLoading the Case Study Data with Jupyter and pandasData Quality Assurance and ExplorationExploring the Financial History Features in the DatasetActivity 1: Exploring Remaining Financial Features in the Dataset
2 - Introduction to Scikit-Learn and Model Evaluation
IntroductionModel Performance Metrics for Binary ClassificationActivity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve
3 - Details of Logistic Regression and Feature Exploration
IntroductionExamining the Relationships between Features and the ResponseUnivariate Feature Selection: What It Does and Doesn't DoBuilding Cloud-Native ApplicationsActivity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients
4 - The Bias-Variance Trade-off
IntroductionEstimating the Coefficients and Intercepts of Logistic RegressionCross Validation: Choosing the Regularization Parameter and Other HyperparametersActivity 4: Cross-Validation and Feature Engineering with the Case Study Data
5 - Decision Trees and Random Forests
IntroductionDecision treesRandom Forests: Ensembles of Decision TreesActivity 5: Cross-Validation Grid Search with Random Forest
6 - Imputation of Missing Data, Financial Analysis, and Delivery to Client
IntroductionReview of Modeling ResultsDealing with Missing Data: Imputation StrategiesActivity 6: Deriving Financial InsightsFinal Thoughts on Delivering the Predictive Model to the Client
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.