Logistic Regression, LDA &KNN in Python
Logistic regression in Python. Logistic Regression , Discriminant Analysis & KNN machine learning models in Python
Created by Abhishek and Pukhraj, Last Updated 27-Oct-2019, Language:English
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.
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.
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.
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
Requirements
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
What Will I Get ?
- 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
- 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
- Learn how to solve real life problem using the different classification techniques
- Data representation using Seaborn library in Python
Course Content
-
Introduction
3 Lectures
00:27:37
-
Welcome to the course!
Preview00:02:52 -
Introduction to Machine Learning
Preview00:16:03 -
Building a Machine Learning model
Preview00:08:42
-
-
Basics of Statistics
5 Lectures
00:30:08
-
Types of Data
Preview00:04:04 -
Types of Statistics
Preview00:02:45 -
Describing data Graphically
00:11:37 -
Measures of Centers
00:07:05 -
Measures of Dispersion
00:04:37
-
-
Setting up Python and Jupyter Notebook
9 Lectures
01:37:58
-
Installing Python and Anaconda
00:03:04 -
Opening Jupyter Notebook
00:09:06 -
Introduction to Jupyter
00:13:26 -
Arithmetic operators in Python: Python Basics
00:04:28 -
Strings in Python: Python Basics
00:19:07 -
Lists, Tuples and Directories: Python Basics
00:18:41 -
Working with Numpy Library of Python
00:11:54 -
Working with Pandas Library of Python
00:09:15 -
Working with Seaborn Library of Python
00:08:57
-
-
Data Preprocessing
15 Lectures
01:24:08
-
Gathering Business Knowledge
00:03:26 -
Data Exploration
00:03:19 -
The Dataset and the Data Dictionary
00:08:14 -
Data Import in Python
00:04:56 -
Univariate analysis and EDD
00:03:33 -
EDD in Python
00:18:01 -
Outlier Treatment
00:04:15 -
Outlier treatment in Python
00:09:53 -
Missing Value Imputation
00:03:36 -
Missing Value Imputation in Python
00:04:49 -
Seasonality in Data
00:03:34 -
Variable Transformation
00:01:02 -
Variable transformation and Deletion in Python
00:04:55 -
Dummy variable creation: Handling qualitative data
00:04:50 -
Dummy variable creation in Python
00:05:45
-
-
Classification Models
20 Lectures
02:06:07
-
Three Classifiers and the problem statement
00:03:17 -
Why can't we use Linear Regression?
00:04:32 -
Logistic Regression
00:07:54 -
Training a Simple Logistic Model in Python
00:12:25 -
Result of Simple Logistic Regression
00:05:11 -
Logistic with multiple predictors
00:02:22 -
Training multiple predictor Logistic model in Python
00:06:05 -
Confusion Matrix
00:03:47 -
Creating Confusion Matrix in Python
00:09:55 -
Evaluating performance of model
00:07:40 -
Evaluating model performance in Python
00:02:21 -
Linear Discriminant Analysis
00:09:42 -
LDA in Python
00:02:30 -
Test-Train Split
00:09:30 -
Test-Train Split in Python
00:06:46 -
K-Nearest Neighbors classifier
00:08:41 -
K-Nearest Neighbors in Python: Part 1
00:05:51 -
K-Nearest Neighbors in Python: Part 2
00:07:00 -
Understanding the results of classification models
00:06:06 -
Summary of the three models
00:04:32
-
-
Attachement Files
1 Lectures
-
Attachement Files
-

Abhishek And Pukhraj
Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.