Linear Regression and Logistic Regression in Python

BUILD PREDICTIVE ML MODELS WITH NO CODING OR MATHS BACKGROUND. LINEAR REGRESSION AND LOGISTIC REGRESSION FOR BEGINNERS.


Course content:

Section 1: Introduction
Section 2: Setting up Python and Python Crash Course
Section 3: Basics of Statistics
Section 4: Data Preprocessing before building Linear Regression Model
Section 5:  Building the Linear Regression Model
Section 6: Logistic Regression: Data Preprocessing
Section 7: Building a Logistic Regression Model
Section 8: Test-Train Split

Total Course Duration:

8.5 hours on-demand video.


This course is for:

  • People pursuing a career in data science. 
  • Working Professionals beginning their Data journey. 
  • Statisticians needing more practical experience. 
  • Anyone curious to master Linear and Logistic Regression from beginner to advanced level in a short span of time.

What you will learn:

  • Learn how to solve real life problem using the Linear and Logistic Regression technique.
  • Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight. 
  • Basic statistics using Numpy library in Python.
  • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python.
  • Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis.
  • Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression probel
  • Data representation using Seaborn library in Python.

Requirements:

  • This course starts from basics and you do not even need coding background to build these models in Python
  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

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