Udemy - Machine learning in Python with Google Colab

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.2 GB
  • Uploaded Byfreecoursewb
  • Downloads120
  • Last checkedJun. 07th '26
  • Date uploadedJun. 05th '26
  • Seeders 7
  • Leechers10

Infohash : 1F576EDC458EF478F31BE4FFCED8E00DD25399E4

Machine learning in Python with Google Colab

https://WebToolTip.com

Published 5/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 56m | Size: 1.19 GB

Machine Learning with Statistical Analysis and Data Visualization in Python using Google Colab

What you'll learn
Prepare, clean, and explore datasets for machine learning
Learn Python basics for machine learning using Google Colab
Build practical machine learning models step by step in Python
Evaluate model performance using accuracy, confusion matrix, and other metrics

Requirements
Basic computer knowledge is enough. No previous machine learning experience is required.

Files:

[ WebToolTip.com ] Udemy - Machine learning in Python with Google Colab
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. Run an Example Python Code in Google Colab.mp4 (15.2 MB)
    • 2. Open Google Colab and Run Your First Python Command (Description).html (0.9 KB)
    • 2. Open Google Colab and Run Your First Python Command.mp4 (29.9 MB)
    • 3. Reopen Google Colab and Continue Your Notebook (Description).html (0.9 KB)
    • 3. Reopen Google Colab and Continue Your Notebook.mp4 (13.5 MB)
    • 4. Add Headings and Subheadings Using Text Cells (Description).html (0.9 KB)
    • 4. Add Headings and Subheadings Using Text Cells.mp4 (8.2 MB)
    • 5. Variables and Data Types in Python.mp4 (40.3 MB)
    • 6. Download Course Data and Code Files.mp4 (7.3 MB)
    2 - Data Handling with Pandas in Python
    • 10. Basic Tasks After Opening Data in Python (Description).html (0.9 KB)
    • 10. Basic Tasks After Opening Data in Python.mp4 (68.4 MB)
    • 7. Data_read_code.txt (0.2 KB)
    • 7. Open a Dataset from a Folder from PC (Description).html (0.8 KB)
    • 7. Open a Dataset from a Folder from PC.mp4 (83.6 MB)
    • 7. data_new.csv (240.0 KB)
    • 7. my_data.xlsx (313.9 KB)
    • 8. Connect Google Drive for Importing Data from Google Drive (Description).html (0.9 KB)
    • 8. Connect Google Drive for Importing Data from Google Drive.mp4 (25.4 MB)
    • 8. Data_from_google_drive.txt (0.3 KB)
    • 9. Import Data into Colab from Google Drive (Description).html (0.9 KB)
    • 9. Import Data into Colab from Google Drive.mp4 (75.0 MB)
    3 - Data Visualization in Python
    • 11. Box Plot for Continuous Data Do Not Just Run the Code Learn It (Description).html (1.0 KB)
    • 11. Box Plot for Continuous Data Do Not Just Run the Code Learn It.mp4 (11.5 MB)
    • 11. BoxPlot.txt (1.3 KB)
    • 12. Variables for Boxplot in Python (Description).html (0.8 KB)
    • 12. Variables for Boxplot in Python.mp4 (66.8 MB)
    • 13. Python Code for Boxplot (Description).html (0.8 KB)
    • 13. Python Code for Boxplot.mp4 (38.8 MB)
    • 14. Boxplot Formatting in Python (Description).html (0.8 KB)
    • 14. Boxplot Formatting in Python.mp4 (55.3 MB)
    • 15. Set Axis and Legend Labels for Boxplot (Description).html (0.8 KB)
    • 15. Set Axis and Legend Labels for Boxplot.mp4 (96.3 MB)
    4 - Data summary descriptive and inferential statistic
    • 16. Descrip_Univar_Bivar.txt (1.6 KB)
    • 16. Variable Preparation for Descriptive Summary (Description).html (0.9 KB)
    • 16. Variable Preparation for Descriptive Summary.mp4 (64.5 MB)
    • 17. Univariate Analysis in Python (Description).html (0.8 KB)
    • 17. Univariate Analysis in Python.mp4 (50.1 MB)
    • 18. Bivariate Analysis in Python (Description).html (0.8 KB)
    • 18. Bivariate Analysis in Python.mp4 (49.1 MB)
    5 - Data Management for Machine Learning in Python
    • 19. ML_data Managemnt.txt (1.6 KB)
    • 19. Python Libraries for Machine Learning Dtata management (Description).html (0.8 KB)
    • 19. Python Libraries for Machine Learning Dtata management.mp4 (42.5 MB)
    • 20. Define Class and Feature Variables in Python for machine learning (Description).html (0.8 KB)
    • 20. Define Class and Feature Variables in Python for machine learning.mp4 (77.6 MB)
    • 21. Drop Missing Values (Description).html (0.7 KB)
    • 21. Drop Missing Values.mp4 (48.4 MB)
    • 22. Prepare the Class and Features (Description).html (0.7 KB)
    • 22. Prepare the Class and Features.mp4 (17.8 MB)
    • 23. Create Training and Testing Data (Description).html (0.7 KB)
    • 23. Create Training and Testing Data.mp4 (21.7 MB)
    • 24. Data Preprocessing in Python (Description).html (0.8 KB)
    • 24. Data Preprocessing in Python.mp4 (48.0 MB)
    6 - Classification Models in Python - LR, DT, KNN, SVM, RF
    • 25. Classification Models in Python.txt (3.5 KB)
    • 25. Libraries for Classification Models in Python (Description).html (0.8 KB)
    • 25. Libraries for Classification Models in Python.mp4 (18.5 MB)
    • 26. Logistic Regression Classifier in Python (Description).html (0.8 KB)
    • 26. Logistic Regression Classifier in Python.mp4 (50.5 MB)
    • 27. Random Forest Classifier in Python (Description).html (0.9 KB)
    • 27. Random Forest Classifier in Python.mp4 (35.1 MB)
    • 28. Build Multiple Classification Models in Python (Description).html (0.9 KB)
    • 28. Build Multiple Classification Models in Python.mp4 (59.1 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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