Regression Analysis with Python

This Course Includes:

Get the knowledge to use Python for building fast and better linear models and to deploy the resulting models in Python with a Regression Analysis with Python course.  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, and Achieving Generalization

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

Lessons 3: Approaching Simple Linear Regression

  • Defining a regression problem
  • Starting from the basics
  • Extending to linear regression
  • Minimising the cost function

Lessons 4: Multiple Regression in Action

  • Using multiple features
  • Revisiting gradient descent
  • Estimating feature importance
  • Interaction models
  • Polynomial regression

Lessons 5: Logistic Regression

  • Defining a classification problem
  • Defining a probability-based approach
  • Revisiting gradient descent
  • Multiclass Logistic Regression

Lessons 6: Data Preparation

  • Numeric feature scaling
  • Qualitative feature encoding
  • Numeric feature transformation
  • Missing data
  • Outliers

Lessons 7: Achieving Generalisation

  • Checking on out-of-sample data
  • Greedy selection of features
  • Regularisation optimized by grid-search
  • Stability selection

Lessons 8: Online and Batch Learning

  • Batch learning
  • Online mini-batch learning

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

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

Hands-on LAB Activities (Performance Labs)

Approaching Simple Linear Regression

Multiple Regression in Action

Logistic Regression

Data Preparation

Achieving Generalisation

Online and Batch Learning

Advanced Regression Methods

Exam FAQs

FAQ's are not Available for this course.



Regression Analysis - Python


10+ Lessons

Delivery Method:




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