Udemy - Machine Learning Real World projects in Python

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Machine Learning Real World projects in Python [TutsNode.com] - Machine Learning Real World projects in Python 3. Project 3-- Predict Prices of Airline Tickets
  • 15. How to Cross Validate your model.mp4 (123.5 MB)
  • 13. Intuition Behind Linear Regression- Part 3.srt (28.4 KB)
  • 15. How to Cross Validate your model.srt (23.1 KB)
  • 7. How to Perform Label Encoding on dataset.srt (18.9 KB)
  • 6. Handle Categorical Data & Feature Encoding.srt (16.4 KB)
  • 3. Understand your data.srt (16.2 KB)
  • 11. Intuition Behind Linear Regression- Part 1.srt (15.9 KB)
  • 4. How to extract Derived features from data.srt (15.1 KB)
  • 10. Apply Random Forest on Data & Automate your predictions.srt (14.8 KB)
  • 1. Introduction to Business Problem & Dataset.srt (2.0 KB)
  • 2. Datasets & Resources.html (0.2 KB)
  • 12. Intuition Behind Linear Regression- Part 2.srt (14.0 KB)
  • 5. Perform Data Pre-processing.srt (11.0 KB)
  • 14. Play with multiple Algorithms & dumping your model.srt (10.8 KB)
  • 8. Outliers Detection in Data.srt (10.4 KB)
  • 9. Select best Features using Feature Selection Technique.srt (7.0 KB)
  • 7. How to Perform Label Encoding on dataset.mp4 (108.7 MB)
  • 6. Handle Categorical Data & Feature Encoding.mp4 (85.5 MB)
  • 13. Intuition Behind Linear Regression- Part 3.mp4 (81.8 MB)
  • 4. How to extract Derived features from data.mp4 (80.4 MB)
  • 10. Apply Random Forest on Data & Automate your predictions.mp4 (75.0 MB)
  • 3. Understand your data.mp4 (70.4 MB)
  • 14. Play with multiple Algorithms & dumping your model.mp4 (60.2 MB)
  • 8. Outliers Detection in Data.mp4 (53.7 MB)
  • 5. Perform Data Pre-processing.mp4 (53.5 MB)
  • 12. Intuition Behind Linear Regression- Part 2.mp4 (44.4 MB)
  • 11. Intuition Behind Linear Regression- Part 1.mp4 (40.9 MB)
  • 9. Select best Features using Feature Selection Technique.mp4 (37.0 MB)
  • 1. Introduction to Business Problem & Dataset.mp4 (21.7 MB)
2. Project 1-- Predict status of Hotel Booking
  • 23. Intuition Behind Random Forest Part-1.srt (26.6 KB)
  • 20. Intuition Behind Decision Tree- Part 4.srt (23.4 KB)
  • 18. Intuition Behind Decision Tree- Part 2.srt (21.9 KB)
  • 21. Intuition Behind Decision Tree- Part 5.srt (20.6 KB)
  • 1. Introduction to Business Problem & Dataset.srt (2.7 KB)
  • 2. Datasets & Resources.html (0.2 KB)
  • 19. Intuition Behind Decision Tree- Part 3.srt (20.0 KB)
  • 4. Analysing Home country of guests.srt (19.1 KB)
  • 15. Idea Behind Cross Validation- Part 2.srt (17.6 KB)
  • 25. Intuition Behind KNN- Part 1.srt (17.5 KB)
  • 7. Select Important features using Machine learning.srt (17.3 KB)
  • 13. Intuition behind Logistic Regression --part 2.srt (17.3 KB)
  • 5. Analysing Prices of Hotels across year.srt (17.2 KB)
  • 24. Intuition Behind Random Forest Part-2.srt (15.4 KB)
  • 12. Intuition behind Logistic Regression --part 1.srt (15.2 KB)
  • 11. Applying Techniques of Feature Importance.srt (14.7 KB)
  • 3. Prepare your data for Analysis & Modelling.srt (14.6 KB)
  • 29. Applying Multiple algorithms on data.srt (14.5 KB)
  • 9. How to handle Categorical data.srt (14.5 KB)
  • 17. Intuition Behind Decision Tree- Part 1.srt (14.0 KB)
  • 7. Select Important features using Machine learning.mp4 (119.3 MB)
  • 14. Idea Behind Cross Validation- Part 1.srt (13.6 KB)
  • 16. Applying logistic regression on data & cross-validate it.srt (13.5 KB)
  • 22. Intuition Behind Decision Tree- Part 6.srt (13.3 KB)
  • 28. Intuition Behind KNN- Part 4.srt (11.0 KB)
  • 26. Intuition Behind KNN- Part 2.srt (10.9 KB)
  • 8. How to extract Derived features from data.srt (10.6 KB)
  • 10. How to Handle Outliers.srt (10.6 KB)
  • 6. Analysing Demand Of hotels.srt (10.3 KB)
  • 27. Intuition Behind KNN- Part 3.srt (9.9 KB)
  • 4. Analysing Home country of guests.mp4 (105.9 MB)
  • 5. Analysing Prices of Hotels across year.mp4 (101.4 MB)
  • 9. How to handle Categorical data.mp4 (91.4 MB)
  • 11. Applying Techniques of Feature Importance.mp4 (85.4 MB)
  • 29. Applying Multiple algorithms on data.mp4 (85.1 MB)
  • 16. Applying logistic regression on data & cross-validate it.mp4 (82.1 MB)
  • 23. Intuition Behind Random Forest Part-1.mp4 (77.8 MB)
  • 3. Prepare your data for Analysis & Modelling.mp4 (75.3 MB)
  • 20. Intuition Behind Decision Tree- Part 4.mp4 (73.5 MB)
  • 15. Idea Behind Cross Validation- Part 2.mp4 (67.3 MB)
  • 8. How to extract Derived features from data.mp4 (66.4 MB)
  • 18. Intuition Behind Decision Tree- Part 2.mp4 (63.2 MB)
  • 6. Analysing Demand Of hotels.mp4 (62.3 MB)
  • 21. Intuition Behind Decision Tree- Part 5.mp4 (62.1 MB)
  • 19. Intuition Behind Decision Tree- Part 3.mp4 (60.7 MB)
  • 12. Intuition behind Logistic Regression --part 1.mp4 (58.0 MB)
  • 10. How to Handle Outliers.mp4 (52.8 MB)
  • 24. Intuition Behind Random Forest Part-2.mp4 (50.8 MB)
  • 13. Intuition behind Logistic Regression --part 2.mp4 (44.1 MB)
  • 14. Idea Behind Cross Validation- Part 1.mp4 (41.7 MB)
  • 25. Intuition Behind KNN- Part 1.mp4 (41.1 MB)
  • 17. Intuition Behind Decision Tree- Part 1.mp4 (40.6 MB)
  • 22. Intuition Behind Decision Tree- Part 6.mp4 (39.6 MB)
  • 28. Intuition Behind KNN- Part 4.mp4 (38.0 MB)
  • 26. Intuition Behind KNN- Part 2.mp4 (33.8 MB)
  • 1. Introduction to Business Problem & Dataset.mp4 (28.6 MB)
  • 27. Intuition Behind KNN- Part 3.mp4 (28.4 MB)
1. Intro to this course
  • 2. How to follow this course-Must Watch.srt (2.8 KB)
  • 4. Regarding Healthcare Project.html (0.5 KB)
  • 3. Installation of Anaconda Navigator.srt (5.3 KB)
  • 1. Introduction & Course Benefits.srt (4.9 KB)
  • 1. Introduction & Course Benefits.mp4 (37.1 MB)
  • 3. Installation of Anaconda Navigator.mp4 (28.7 MB)
  • 2. How to follow this course-Must Watch.mp4 (17.9 MB)
  • TutsNode.com.txt (0.1 KB)
    • .pad
    • 0 (0.1 KB)
    • 1 (570.9 KB)
    • 2 (350.8 KB)
    • 3 (140.2 KB)
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Description


Description

Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology.

This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition:
So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand.

1.Task #1 @Predicting the Hotel booking : Predict Whether booking is going to cancel or not

3.Task #2 @Predict Whether Person has a Chronic Disease or not: Develop a Machine learning Model that predicts whether person has kidney disease or not

2.Task #3 @Predict the Prices of Flight: Predict the prices of Flght using Regression & Ensemble Algorithms..

The course covers a number of different machine learning algorithms such as Regression and Classification algorithms. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 3 brand new projects that can help you experience the power of Machine Learning using real-world examples!
Who this course is for:

Data Scientists who want to apply their knowledge on Real World Case Studies
Machine Learning Enthusiasts who look to add more projects to their Portfolio

Requirements

Basic knowledge of Python programming is recommended.

Last Updated 2/2021



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Udemy - Machine Learning Real World projects in Python

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2.8 GB
seeders:21
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Udemy - Machine Learning Real World projects in Python

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