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In this course, you will learn:
- How to Import data sets
- How to Clean and prepare data for analysis
- How to Manipulate pandas DataFrame
- How to Summarize data
- How to Build machine learning models using scikit-learn
- How to Build data pipelines
Syllabus:
1. Importing Datasets
- Learning Objectives
- Understanding the Domain
- Understanding the Dataset
- Python package for data science
- Importing and Exporting Data in Python
- Basic Insights from Datasets
2. Cleaning and Preparing the Data
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization Sets
- Binning
- Indicator variables
3. Summarizing the Data Frame
- Descriptive Statistics
- Basic of Grouping
- ANOVA
- Correlation
- More on Correlation
4. Model Development
- Simple and Multiple Linear Regression
- Model Evaluation Using Visualization
- Polynomial Regression and Pipelines
- R-squared and MSE for In-Sample Evaluation
- Prediction and Decision Making
5. Model Evaluation
- Model Evaluation
- Over-fitting, Under-fitting, and Model Selection
- Ridge Regression
- Grid Search
- Model Refinement
