# Linear Regression and Logistic Regression in Python

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners

4.3/5

## Linear Regression and Logistic Regression in Python

### What you’ll learn

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

### Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Setting up Python and Python Crash Course

Lecture 3 Installing Python and Anaconda

Lecture 4 This is a milestone!

Lecture 5 Opening Jupyter Notebook

Lecture 6 Introduction to Jupyter

Lecture 7 Arithmetic operators in Python: Python Basics

Lecture 8 Strings in Python: Python Basics

Lecture 9 Lists, Tuples and Directories: Python Basics

Lecture 10 Working with Numpy Library of Python

Lecture 11 Working with Pandas Library of Python

Lecture 12 Working with Seaborn Library of Python

Section 3: Basics of Statistics

Lecture 13 Types of Data

Lecture 14 Types of Statistics

Lecture 15 Describing data Graphically

Lecture 16 Measures of Centers

Lecture 17 Measures of Dispersion

Section 4: Data Preprocessing before building Linear Regression Model

Lecture 19 Data Exploration

Lecture 20 The Dataset and the Data Dictionary

Lecture 21 Importing Data in Python

Lecture 22 Univariate analysis and EDD

Lecture 23 EDD in Python

Lecture 24 Outlier Treatment

Lecture 25 Outlier Treatment in Python

Lecture 26 Missing Value Imputation

Lecture 27 Missing Value Imputation in Python

Lecture 28 Seasonality in Data

Lecture 29 Bi-variate analysis and Variable transformation

Lecture 30 Variable transformation and deletion in Python

Lecture 31 Non-usable variables

Lecture 32 Dummy variable creation: Handling qualitative data

Lecture 33 Dummy variable creation in Python

Lecture 34 Correlation Analysis

Lecture 35 Correlation Analysis in Python

Section 5: Building the Linear Regression Model

Lecture 36 The Problem Statement

Lecture 37 Basic Equations and Ordinary Least Squares (OLS) method

Lecture 38 Assessing accuracy of predicted coefficients

Lecture 39 Assessing Model Accuracy: RSE and R squared

Lecture 40 Simple Linear Regression in Python

Lecture 41 Multiple Linear Regression

Lecture 42 The F – statistic

Lecture 43 Interpreting results of Categorical variables

Lecture 44 Multiple Linear Regression in Python

Lecture 45 Test-train split

Lecture 47 More about test-train split

Lecture 48 Test train split in Python

Section 6: Introduction to the classification Models

Lecture 49 Three classification models and Data set

Lecture 50 Importing the data into Python

Lecture 51 The problem statements

Lecture 52 Why can’t we use Linear Regression?

Section 7: Building a Logistic Regression Model

Lecture 53 Logistic Regression

Lecture 54 Training a Simple Logistic Model in Python

Lecture 55 Result of Simple Logistic Regression

Lecture 56 Logistic with multiple predictors

Lecture 57 Training multiple predictor Logistic model in Python

Lecture 58 Confusion Matrix

Lecture 59 Creating Confusion Matrix in Python

Lecture 60 Evaluating performance of model

Lecture 61 Evaluating model performance in Python

Section 8: Test-Train Split

Lecture 62 Test-Train Split

Lecture 63 Test-Train Split in Python

Lecture 64 The final milestone!

Lecture 65 Bonus Lecture

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

#### Course Information:

Udemy | English | 7h 33m | 2.22 GB