Logistic Regression in Python

Logistic Regression in Python

Logistic Regression in Python

Logistic regression in Python tutorial for beginners. You can do Predictive modeling using Python after this course.

Language: english

Note: 4.5/5 (799 notes) 97,554 students

Instructor(s): Start-Tech Academy

Last update: 2022-01-15

What you’ll learn

  • Understand how to interpret the result of Logistic Regression model in Python and translate them into actionable insight
  • Learn the linear discriminant analysis and K-Nearest Neighbors technique in Python
  • Preliminary analysis of data using Univariate analysis before running classification model
  • Predict future outcomes basis past data by implementing Machine Learning algorithm
  • Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
  • Learn how to solve real life problem using the different classification techniques
  • Course contains a end-to-end DIY project to implement your learnings from the lectures
  • Basic statistics using Numpy library in Python
  • Data representation using Seaborn library in Python
  • Classification techniques of Machine Learning using Scikit Learn and Statsmodel libraries of Python

 

Requirements

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

 

Description

You’re looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in Python, right?

You’ve found the right Classification modeling course!

After completing this course you will be able to:

  • Identify the business problem which can be solved using Classification modeling techniques of Machine Learning.

  • Create different Classification modelling model in Python and compare their performance.

  • Confidently practice, discuss and understand Machine Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem using classification techniques.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman – Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a classification model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Classification Machine Learning models:

  • Section 1 – Basics of Statistics

    This section is divided into five different lectures starting from types of data then types of statistics

    then graphical representations to describe the data and then a lecture on measures of center like mean

    median and mode and lastly measures of dispersion like range and standard deviation

  • Section 2 – Python basic

    This section gets you started with Python.

    This section will help you set up the python and Jupyter environment on your system and it’ll teach

    you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Section 3 – Introduction to Machine Learning

    In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

  • Section 4 – Data Pre-processing

    In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

    We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.

  • Section 5 – Classification Models

    This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don’t understand

    it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a classification model in Python will soar. You’ll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems.


Go ahead and click the enroll button, and I’ll see you in lesson 1!


Cheers

Start-Tech Academy


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Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Which all classification techniques are taught in this course?

In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:

  1. Logistic Regression

  2. Linear Discriminant Analysis

  3. K – Nearest Neighbors (KNN)

How much time does it take to learn Classification techniques of machine learning?

Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of  models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use Python for Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

 

Who 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 classification machine learning techniques from Beginner to Advanced in short span of time

 

Course content

  • Introduction
    • Welcome to the course!
    • Course Resources
  • Introduction to Machine Learning
    • Introduction to Machine Learning
    • This is a milestone!
    • Building a Machine Learning model
  • Basics of Statistics
    • Types of Data
    • Types of Statistics
    • Describing data Graphically
    • Measures of Centers
    • Practice Exercise 1
    • Measures of Dispersion
    • Practice Exercise 2
  • Setting up Python and Jupyter Notebook
    • Installing Python and Anaconda
    • Opening Jupyter Notebook
    • Introduction to Jupyter
    • Arithmetic operators in Python: Python Basics
    • Strings in Python: Python Basics
    • Lists, Tuples and Directories: Python Basics
    • Working with Numpy Library of Python
    • Working with Pandas Library of Python
    • Working with Seaborn Library of Python
  • Data Preprocessing
    • Gathering Business Knowledge
    • Data Exploration
    • The Dataset and the Data Dictionary
    • Data Import in Python
    • Project Exercise 1
    • Univariate analysis and EDD
    • EDD in Python
    • Project Exercise 2
    • Outlier Treatment
    • Outlier treatment in Python
    • Project Exercise 3
    • Missing Value Imputation
    • Missing Value Imputation in Python
    • Project Exercise 4
    • Seasonality in Data
    • Variable Transformation
    • Variable transformation and Deletion in Python
    • Project Exercise 5
    • Dummy variable creation: Handling qualitative data
    • Dummy variable creation in Python
    • Project Exercise 6
    • Quiz
  • Classification Models
    • Three Classifiers and the problem statement
    • Why can’t we use Linear Regression?
    • Logistic Regression
    • Training a Simple Logistic Model in Python
    • Project Exercise 7
    • Result of Simple Logistic Regression
    • Quiz
    • Logistic with multiple predictors
    • Training multiple predictor Logistic model in Python
    • Project Exercise 8
    • Confusion Matrix
    • Creating Confusion Matrix in Python
    • Evaluating performance of model
    • Evaluating model performance in Python
    • Project Exercise 9
    • Quiz
  • Linear Discriminant Analysis (LDA)
    • Linear Discriminant Analysis
    • LDA in Python
    • Project Exercise 10
  • Test-Train Split
    • Test-Train Split
    • More about test-train split
    • Test-Train Split in Python
    • Project Exercise 11
    • Quiz
  • K-Nearest Neighbors classifier
    • K-Nearest Neighbors classifier
    • K-Nearest Neighbors in Python: Part 1
    • K-Nearest Neighbors in Python: Part 2
    • Project Exercise 12
  • Understanding the Results
    • Understanding the results of classification models
    • Summary of the three models
    • The Final Exercise!
  • Appendix 1: Linear Regression in Python
    • The Problem Statement
    • Basic Equations and Ordinary Least Squares (OLS) method
    • Assessing accuracy of predicted coefficients
    • Assessing Model Accuracy: RSE and R squared
    • Simple Linear Regression in Python
    • Multiple Linear Regression
    • The F – statistic
    • Interpreting results of Categorical variables
    • Multiple Linear Regression in Python
    • Quiz
  • Course Conclusion
    • The final milestone!
    • Bonus lecture

 

Logistic Regression in Python

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