| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1 - 00-Introduction-01-py.pdf | 472.2 KB | ||
| 1 - Welcome To The Course.mp4 | 25.3 MB | ||
| 2 - Course Resources.html | 102.4 B | ||
| Logistic Regression in Python | |||
| Data Files | |||
| Customer.csv | 64 KB | ||
| House-Price.csv | 50.6 KB | ||
| Others | |||
| Classification.ipynb | 171.7 KB | ||
| Presentation | |||
| 02_whynot_linear.pdf | 155.3 KB | ||
| 03_logistic.pdf | 352.7 KB | ||
| 04_.pdf | 165.3 KB | ||
| 04_P_value.pdf | 228 KB | ||
| 05_Multiple_predictors.pdf | 151.3 KB | ||
| 06_Confusion matrix.pdf | 222.3 KB | ||
| 07_LDA.pdf | 183.1 KB | ||
| 08_ROC.pdf | 306.9 KB | ||
| 09_KNN.pdf | 236.7 KB | ||
| 10 - Knearest Neighbors Classifier | |||
| 11 - Understanding The Results | |||
| 12 - Appendix 1 Linear Regression In Python | |||
| 76 - The Problem Statement.mp4 | 14.2 MB | ||
| 77 - Basic Equations And Ordinary Least Squares Ols Method.mp4 | 65.1 MB | ||
| 78 - Assessing Accuracy Of Predicted Coefficients.mp4 | 139.8 MB | ||
| 79 - Assessing Model Accuracy Rse And R Squared.mp4 | 66.6 MB | ||
| 80 - Simple Linear Regression In Python.mp4 | 89.7 MB | ||
| 81 - Multiple Linear Regression.mp4 | 52.4 MB | ||
| 82 - The F Statistic.mp4 | 86.7 MB | ||
| 83 - Interpreting Results Of Categorical Variables.mp4 | 33.6 MB | ||
| 84 - Multiple Linear Regression In Python.mp4 | 100.8 MB | ||
| 85 - Loan-Log.ipynb | 127.3 KB | ||
| 85 - Practical Task 1.html | 1 KB | ||
| 86 - Practical Task 2.html | 1.1 KB | ||
| 87 - Practical Task 3.html | 3.3 KB | ||
| 88 - Comprehensive Interview Preparation Questions.html | 1.4 KB | ||
| ML Practical task 3 | |||
| ML Task 1 | |||
| Loan.ipynb | 213 KB | ||
| Loan.xlsx | 25.6 KB | ||
| Loan.xlsx - loan_data.csv | 19.8 KB | ||
| ML Task 2 | |||
| 13 - Congratulations About Your Certificate | |||
| 2 - Introduction To Machine Learning | |||
| 3 - Basics Of Statistics | |||
| 10 - Exercise-1.pdf | 553.8 KB | ||
| 10 - Practice Exercise 1.html | 307.2 B | ||
| 11 - Measures Of Dispersion.mp4 | 14.4 MB | ||
| 12 - Exercise-2.pdf | 469.9 KB | ||
| 12 - Practice Exercise 2.html | 307.2 B | ||
| 4 - Setting Up Python And Jupyter Notebook | |||
| 13 - Installing Python And Anaconda.mp4 | 15.3 MB | ||
| 14 - Opening Jupyter Notebook.mp4 | 54.7 MB | ||
| 15 - Introduction To Jupyter.mp4 | 36.4 MB | ||
| 16 - Arithmetic Operators In Python Python Basics.mp4 | 10.5 MB | ||
| 17 - Strings In Python Python Basics.mp4 | 81.4 MB | ||
| 18 - Lists Part 1.mp4 | 11.3 MB | ||
| 19 - Lists Part 2.mp4 | 13.4 MB | ||
| 20 - Tuples And Dictionaries.mp4 | 13 MB | ||
| 5 - Important Python Libraries | |||
| 21 - Working With Numpy Library Of Python.mp4 | 52.9 MB | ||
| 22 - Customer.csv | 64 KB | ||
| 22 - Working With Pandas Library Of Python.mp4 | 55.9 MB | ||
| 23 - Working With Seaborn Library Of Python.mp4 | 61.7 MB | ||
| 24 - Python File For Additional Practice.html | 307.2 B | ||
| 24 - Reference-Guide-for-Python-practice.ipynb | 80.2 KB | ||
| 25 - About The Upcoming Role Play.html | 1.1 KB | ||
| 6 - Data Preprocessing | |||
| 26 - Gathering Business Knowledge.mp4 | 8.7 MB | ||
| 27 - Data Exploration.mp4 | 14.6 MB | ||
| 28 - House-Price.csv | 50.6 KB | ||
| 28 - The Dataset And The Data Dictionary.mp4 | 127.4 MB | ||
| 29 - Data Import In Python.mp4 | 35.5 MB | ||
| 29 - House-Price.csv | 50.6 KB | ||
| 30 - Movie-collection.csv | 55.8 KB | ||
| 30 - Project Exercise 1.html | 512 B | ||
| 31 - 03-04-PDE-Univariate-Analysis-Uni.pdf | 333.4 KB | ||
| 31 - Univariate Analysis And Edd.mp4 | 19 MB | ||
| 32 - Edd In Python.mp4 | 123.8 MB | ||
| 33 - Project Exercise 2.html | 204.8 B | ||
| 34 - 04-06-PDE-Outlier-Treatment.pdf | 355.1 KB | ||
| 34 - Outlier Treatment.mp4 | 16.2 MB | ||
| 35 - Outlier Treatment In Python.mp4 | 76.5 MB | ||
| 36 - Project Exercise 3.html | 204.8 B | ||
| 37 - 04-05-PDE-Missing-value.pdf | 315.7 KB | ||
| 37 - Missing Value Imputation.mp4 | 15.4 MB | ||
| 38 - Missing Value Imputation In Python.mp4 | 34.5 MB | ||
| 39 - Project Exercise 4.html | 204.8 B | ||
| 40 - 04-07-PDE-Seasonality.pdf | 364.1 KB | ||
| 40 - Seasonality In Data.mp4 | 12.3 MB | ||
| 41 - 04-07-Variable-Transformation.pdf | 456.1 KB | ||
| 41 - Variable Transformation.mp4 | 22.1 MB | ||
| 42 - Variable Transformation And Deletion In Python.mp4 | 39.9 MB | ||
| 43 - Project Exercise 5.html | 204.8 B | ||
| 44 - 04-11-Dummy-Var.pdf | 163 KB | ||
| 44 - Dummy Variable Creation Handling Qualitative Data.mp4 | 21.7 MB | ||
| 45 - Dummy Variable Creation In Python.mp4 | 43.7 MB | ||
| 46 - Project Exercise 6.html | 204.8 B | ||
| 7 - Classification Models | |||
| 47 - 01-INtro.pdf | 190.4 KB | ||
| 47 - Three Classifiers And The Problem Statement.mp4 | 31.8 MB | ||
| 48 - 02-whynot-linear.pdf | 155.3 KB | ||
| 48 - Why Cant We Use Linear Regression.mp4 | 28 MB | ||
| 49 - 03-logistic.pdf | 352.7 KB | ||
| 49 - Logistic Regression.mp4 | 55 MB | ||
| 50 - Training A Simple Logistic Model In Python.mp4 | 76.1 MB | ||
| 51 - Project Exercise 7.html | 307.2 B | ||
| 52 - 04-P-value.pdf | 228 KB | ||
| 52 - Result Of Simple Logistic Regression.mp4 | 44.3 MB | ||
| 53 - 05-Multiple-predictors.pdf | 151.3 KB | ||
| 53 - Logistic With Multiple Predictors.mp4 | 13.6 MB | ||
| 54 - Training Multiple Predictor Logistic Model In Python.mp4 | 40.9 MB | ||
| 55 - Project Exercise 8.html | 307.2 B | ||
| 56 - 06-Confusion-matrix.pdf | 222.3 KB | ||
| 56 - Confusion Matrix.mp4 | 40.1 MB | ||
| 57 - Creating Confusion Matrix In Python.mp4 | 75.4 MB | ||
| 58 - 08-ROC.pdf | 306.9 KB | ||
| 58 - Evaluating Performance Of Model.mp4 | 60.1 MB | ||
| 59 - Evaluating Model Performance In Python.mp4 | 14.4 MB | ||
| 60 - Project Exercise 9.html | 204.8 B | ||
| 8 - Linear Discriminant Analysis Lda | |||
| 61 - 07-LDA.pdf | 183.1 KB | ||
| 61 - Linear Discriminant Analysis.mp4 | 67.7 MB | ||
| 62 - Lda In Python.mp4 | 17 MB | ||
| 63 - Project Exercise 10.html | 204.8 B | ||
| 9 - Testtrain Split | |||
| 64 - 10-Test-Train.pdf | 238.7 KB | ||
| 64 - Testtrain Split.mp4 | 64.6 MB | ||
| 65 - More About Testtrain Split.html | 512 B | ||
| 66 - Testtrain Split In Python.mp4 | 52.4 MB | ||
| 67 - Project Exercise 11.html | 204.8 B | ||
| 6 - 01-01-Lecture-TypesOfData.pdf | 177.7 KB | ||
| 6 - Types Of Data.mp4 | 20.7 MB | ||
| 7 - 01-02-Lecture-TypesOfStatistics.pdf | 171.7 KB | ||
| 7 - Types Of Statistics.mp4 | 7.9 MB | ||
| 8 - 01-03-Lecture-DataSummaryandGraph.pdf | 317.9 KB | ||
| 8 - Describing Data Graphically.mp4 | 61.5 MB | ||
| 9 - 01-04-Lecture-Centers.pdf | 313 KB | ||
| 9 - Measures Of Centers.mp4 | 29.8 MB | ||
| 3 - Introduction To Machine Learning.mp4 | 145.6 MB | ||
| 3 - Lecture-machineLearning.pdf | 991.6 KB | ||
| 4 - This Is A Milestone.mp4 | 70.7 MB | ||
| 5 - Building A Machine Learning Model.mp4 | 27.7 MB | ||
| 89 - The Final Milestone.mp4 | 8.4 MB | ||
| 90 - About Your Certificate.html | 921.6 B | ||
| 91 - Bonus Lecture.html | 9.1 KB | ||
| OnlineFood.ipynb | 1 MB | ||
| onlinefoods.xlsx - onlinefoods.csv | 21.2 KB | ||
| Restaurant_revenue (1).csv | 88.3 KB | ||
| Resutrants.ipynb | 2 MB | ||
| 72 - 11-results.pdf | 170.9 KB | ||
| 72 - Understanding The Results Of Classification Models.mp4 | 67 MB | ||
| 73 - 12-steps.pdf | 148.1 KB | ||
| 73 - Summary Of The Three Models.mp4 | 35.6 MB | ||
| 74 - The Final Exercise.html | 1.8 KB | ||
| 74 - weekly.csv | 60.1 KB | ||
| 75 - New Ai Features In Python The Latest Updates You Must Know.html | 4.6 KB | ||
| 68 - 09-KNN.pdf | 236.7 KB | ||
| 68 - Knearest Neighbors Classifier.mp4 | 120.4 MB | ||
| 69 - Knearest Neighbors In Python Part 1.mp4 | 56.2 MB | ||
| 70 - Knearest Neighbors In Python Part 2.mp4 | 64.4 MB | ||
| 71 - Project Exercise 12.html | 204.8 B | ||
| 10_Test_Train.pdf | 238.7 KB | ||
| 11_results.pdf | 170.9 KB | ||
| 12_steps.pdf | 148.1 KB | ||
| Python_CrashC1.ipynb | 29.6 KB | ||
| Python_cc2.ipynb | 169.5 KB | ||
| Product.txt | 139.5 KB |
Logistic Regression in Python
https://WebToolTip.com
Last updated 3/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.04 GB | Duration: 7h 40m
Logistic regression in Python tutorial for beginners. You can do Predictive modeling using Python after this course.
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
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Udemy - The Complete Linear and Logistic Regression Course in Python Posted by
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