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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.
Regression Analysis with Python
Test Prep
35+ Pre Assessment Questions | 35+ Post Assessment Questions |

Why choose TOPTALENT?


Lessons 1:

  • 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