Logistic Regression in Python

Logistic Regression in Python, available at $69.99, has an average rating of 4.5, with 92 lectures, 13 quizzes, based on 895 reviews, and has 103419 subscribers.

You will learn about 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 This course is ideal for individuals who are People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master classification machine learning techniques from Beginner to Advanced in short span of time It is particularly useful for People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master classification machine learning techniques from Beginner to Advanced in short span of time.

Enroll now: Logistic Regression in Python

Summary

Title: Logistic Regression in Python

Price: $69.99

Average Rating: 4.5

Number of Lectures: 92

Number of Quizzes: 13

Number of Published Lectures: 86

Number of Published Quizzes: 12

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 98

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the course!

Lecture 2: Course Resources

Chapter 2: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: This is a milestone!

Lecture 3: Building a Machine Learning model

Chapter 3: Basics of Statistics

Lecture 1: Types of Data

Lecture 2: Types of Statistics

Lecture 3: Describing data Graphically

Lecture 4: Measures of Centers

Lecture 5: Practice Exercise 1

Lecture 6: Measures of Dispersion

Lecture 7: Practice Exercise 2

Chapter 4: Setting up Python and Jupyter Notebook

Lecture 1: Installing Python and Anaconda

Lecture 2: Opening Jupyter Notebook

Lecture 3: Introduction to Jupyter

Lecture 4: Arithmetic operators in Python: Python Basics

Lecture 5: Strings in Python: Python Basics

Lecture 6: Lists – Part 1

Lecture 7: Lists – Part 2

Lecture 8: Tuples and Dictionaries

Chapter 5: Important Python libraries

Lecture 1: Working with Numpy Library of Python

Lecture 2: Working with Pandas Library of Python

Lecture 3: Working with Seaborn Library of Python

Lecture 4: Python file for additional practice

Chapter 6: Integrating ChatGPT with Python

Lecture 1: Integrating ChatGPT with Jupyter Notebook

Chapter 7: Data Preprocessing

Lecture 1: Gathering Business Knowledge

Lecture 2: Data Exploration

Lecture 3: The Dataset and the Data Dictionary

Lecture 4: Data Import in Python

Lecture 5: Project Exercise 1

Lecture 6: Univariate analysis and EDD

Lecture 7: EDD in Python

Lecture 8: Project Exercise 2

Lecture 9: Outlier Treatment

Lecture 10: Outlier treatment in Python

Lecture 11: Project Exercise 3

Lecture 12: Missing Value Imputation

Lecture 13: Missing Value Imputation in Python

Lecture 14: Project Exercise 4

Lecture 15: Seasonality in Data

Lecture 16: Variable Transformation

Lecture 17: Variable transformation and Deletion in Python

Lecture 18: Project Exercise 5

Lecture 19: Dummy variable creation: Handling qualitative data

Lecture 20: Dummy variable creation in Python

Lecture 21: Project Exercise 6

Chapter 8: Classification Models

Lecture 1: Three Classifiers and the problem statement

Lecture 2: Why cant we use Linear Regression?

Lecture 3: Logistic Regression

Lecture 4: Training a Simple Logistic Model in Python

Lecture 5: Project Exercise 7

Lecture 6: Result of Simple Logistic Regression

Lecture 7: Logistic with multiple predictors

Lecture 8: Training multiple predictor Logistic model in Python

Lecture 9: Project Exercise 8

Lecture 10: Confusion Matrix

Lecture 11: Creating Confusion Matrix in Python

Lecture 12: Evaluating performance of model

Lecture 13: Evaluating model performance in Python

Lecture 14: Project Exercise 9

Chapter 9: Linear Discriminant Analysis (LDA)

Lecture 1: Linear Discriminant Analysis

Lecture 2: LDA in Python

Lecture 3: Project Exercise 10

Chapter 10: Test-Train Split

Lecture 1: Test-Train Split

Lecture 2: More about test-train split

Lecture 3: Test-Train Split in Python

Lecture 4: Project Exercise 11

Chapter 11: K-Nearest Neighbors classifier

Lecture 1: K-Nearest Neighbors classifier

Lecture 2: K-Nearest Neighbors in Python: Part 1

Lecture 3: K-Nearest Neighbors in Python: Part 2

Lecture 4: Project Exercise 12

Chapter 12: Understanding the Results

Lecture 1: Understanding the results of classification models

Lecture 2: Summary of the three models

Lecture 3: The Final Exercise!

Chapter 13: Appendix 1: Linear Regression in Python

Lecture 1: The Problem Statement

Lecture 2: Basic Equations and Ordinary Least Squares (OLS) method

Instructors

Start-Tech Academy
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Rating Distribution

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2 stars: 24 votes

3 stars: 149 votes

4 stars: 338 votes

5 stars: 371 votes

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