Regression Analysis with Python
Get the knowledge to use Python for building fast and better linear models and to deploy the resulting models in Python with uCertify’s course Regression Analysis with Python. The course provides hands-on experience of the concepts, Regression – The Workhorse of Data Science, Approaching Simple Linear Regression, Multiple Regression in Action. Logistic Regression, Data Preparation, Achieving Generalization, and so on.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
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Test Prep
35+ Pre Assessment Questions |
35+ Post Assessment Questions |
Features
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- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Preface
- What this course covers
- What you need for this course
- Who this course is for
- Conventions
Lessons 2:
Regression – The Workhorse of Data Science
- Regression analysis and data science
- Python for data science
- Python packages and functions for linear models
- Summary
Lessons 3:
Approaching Simple Linear Regression
- Defining a regression problem
- Starting from the basics
- Extending to linear regression
- Minimizing the cost function
- Summary
Lessons 4:
Multiple Regression in Action
- Using multiple features
- Revisiting gradient descent
- Estimating feature importance
- Interaction models
- Polynomial regression
- Summary
Lessons 5:
Logistic Regression
- Defining a classification problem
- Defining a probability-based approach
- Revisiting gradient descent
- Multiclass Logistic Regression
- An example
- Summary
Lessons 6:
Data Preparation
- Numeric feature scaling
- Qualitative feature encoding
- Numeric feature transformation
- Missing data
- Outliers
- Summary
Lessons 7:
Achieving Generalization
- Checking on out-of-sample data
- Greedy selection of features
- Regularization optimized by grid-search
- Stability selection
- Summary
Lessons 8:
Online and Batch Learning
- Batch learning
- Online mini-batch learning
- Summary
Lessons 9:
Advanced Regression Methods
- Least Angle Regression
- Bayesian regression
- SGD classification with hinge loss
- Regression trees (CART)
- Bagging and boosting
- Gradient Boosting Regressor with LAD
- Summary
Lessons 10:
Real-world Applications for Regression Models
- Downloading the datasets
- A regression problem
- An imbalanced and multiclass classification problem
- A ranking problem
- A time series problem
- Summary
