| 1. Competitions on Kaggle Lesson 1.mp4 | 174.9 MB | ||
| 1. Competitions on Kaggle Lesson 1.srt | 30.9 KB | ||
| 1. Courses in Kaggle.mp4 | 48 MB | ||
| 1. Courses in Kaggle.srt | 8.7 KB | ||
| 1. Datasets on Kaggle.mp4 | 122.6 MB | ||
| 1. Datasets on Kaggle.srt | 22.4 KB | ||
| 1. Dropping Columns with Low Correlation.mp4 | 24.8 MB | ||
| 1. Dropping Columns with Low Correlation.srt | 5.2 KB | ||
| 1. Examining Missing Values.mp4 | 42.4 MB | ||
| 1. Examining Missing Values.srt | 13.2 KB | ||
| 1. Examining the Code Section in Kaggle Lesson 1.mp4 | 73.4 MB | ||
| 1. Examining the Code Section in Kaggle Lesson 1.srt | 18.1 KB | ||
| 1. First Step to the Hearth Attack Prediction Project.mp4 | 108.6 MB | ||
| 1. First Step to the Hearth Attack Prediction Project.srt | 21.4 KB | ||
| 1. Kaggle Masterclass with Hearth Attack Prediction Project.html | 307.2 B | ||
| 1. Logistic Regression.mp4 | 27.3 MB | ||
| 1. Logistic Regression.srt | 9.1 KB | ||
| 1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 | 74.6 MB | ||
| 1. Numeric Variables (Analysis with Distplot) Lesson 1.srt | 20.2 KB | ||
| 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 | 45.4 MB | ||
| 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.srt | 11.3 KB | ||
| 1. Project Conclusion and Sharing.mp4 | 27 MB | ||
| 1. Project Conclusion and Sharing.srt | 4.9 KB | ||
| 1. Required Python Libraries.mp4 | 58.7 MB | ||
| 1. Required Python Libraries.srt | 12.8 KB | ||
| 1. User Page Review on Kaggle.mp4 | 74.8 MB | ||
| 1. User Page Review on Kaggle.srt | 13.9 KB | ||
| 1. What is Discussion on Kaggle.mp4 | 37.8 MB | ||
| 1. What is Discussion on Kaggle.srt | 7.7 KB | ||
| 1. What is Kaggle.mp4 | 122.4 MB | ||
| 1. What is Kaggle.srt | 23 KB | ||
| 10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 | 10.6 MB | ||
| 10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.srt | 3.1 KB | ||
| 10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 | 64 MB | ||
| 10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.srt | 15.4 KB | ||
| 11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 | 36 MB | ||
| 11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.srt | 10.1 KB | ||
| 11. Separating Data into Test and Training Set.mp4 | 27.8 MB | ||
| 11. Separating Data into Test and Training Set.srt | 9.4 KB | ||
| 12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 | 32.8 MB | ||
| 12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.srt | 10.3 KB | ||
| 13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 | 33.7 MB | ||
| 13. Relationships between variables (Analysis with Heatmap) Lesson 1.srt | 8.7 KB | ||
| 14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 | 82.5 MB | ||
| 14. Relationships between variables (Analysis with Heatmap) Lesson 2.srt | 16 KB | ||
| 2. Competitions on Kaggle Lesson 2.mp4 | 179.5 MB | ||
| 2. Competitions on Kaggle Lesson 2.srt | 30 KB | ||
| 2. Cross Validation.mp4 | 28.2 MB | ||
| 2. Cross Validation.srt | 7.6 KB | ||
| 2. Examining Unique Values.mp4 | 41 MB | ||
| 2. Examining Unique Values.srt | 12.9 KB | ||
| 2. Examining the Code Section in Kaggle Lesson 2.mp4 | 98 MB | ||
| 2. Examining the Code Section in Kaggle Lesson 2.srt | 21.3 KB | ||
| 2. FAQ about Kaggle.html | 10.9 KB | ||
| 2. FAQ about Machine Learning, Data Science.html | 15.3 KB | ||
| 2. Loading the Statistics Dataset in Data Science.mp4 | 9.3 MB | ||
| 2. Loading the Statistics Dataset in Data Science.srt | 2.7 KB | ||
| 2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 | 18.3 MB | ||
| 2. Numeric Variables (Analysis with Distplot) Lesson 2.srt | 5.3 KB | ||
| 2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 | 32.8 MB | ||
| 2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.srt | 9.7 KB | ||
| 2. Ranking Among Users on Kaggle.mp4 | 99.2 MB | ||
| 2. Ranking Among Users on Kaggle.srt | 19.7 KB | ||
| 2. Treasure in The Kaggle.mp4 | 69.7 MB | ||
| 2. Treasure in The Kaggle.srt | 10.5 KB | ||
| 2. Visualizing Outliers.mp4 | 32.7 MB | ||
| 2. Visualizing Outliers.srt | 11.9 KB | ||
| 3. Blog and Documentation Sections.mp4 | 38.8 MB | ||
| 3. Blog and Documentation Sections.srt | 6.5 KB | ||
| 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 | 69 MB | ||
| 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.srt | 19.7 KB | ||
| 3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 | 22.3 MB | ||
| 3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.srt | 5 KB | ||
| 3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 | 40 MB | ||
| 3. Dealing with Outliers – Trtbps Variable Lesson 1.srt | 13.7 KB | ||
| 3. Examining the Code Section in Kaggle Lesson 3.mp4 | 148.5 MB | ||
| 3. Examining the Code Section in Kaggle Lesson 3.srt | 27.7 KB | ||
| 3. Initial analysis on the dataset.mp4 | 58.6 MB | ||
| 3. Initial analysis on the dataset.srt | 18.2 KB | ||
| 3. Notebook Design to be Used in the Project.mp4 | 97.7 MB | ||
| 3. Notebook Design to be Used in the Project.srt | 20.3 KB | ||
| 3. Publishing Notebooks on Kaggle.mp4 | 35.4 MB | ||
| 3. Publishing Notebooks on Kaggle.srt | 7.2 KB | ||
| 3. Registering on Kaggle and Member Login Procedures.mp4 | 40.5 MB | ||
| 3. Registering on Kaggle and Member Login Procedures.srt | 9.5 KB | ||
| 3. Roc Curve and Area Under Curve (AUC).mp4 | 38.6 MB | ||
| 3. Roc Curve and Area Under Curve (AUC).srt | 10.2 KB | ||
| 3. Separating variables (Numeric or Categorical).mp4 | 14.7 MB | ||
| 3. Separating variables (Numeric or Categorical).srt | 4.6 KB | ||
| 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 | 78 MB | ||
| 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.srt | 20.9 KB | ||
| 4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 | 52.3 MB | ||
| 4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.srt | 16.7 KB | ||
| 4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 | 40.8 MB | ||
| 4. Dealing with Outliers – Trtbps Variable Lesson 2.srt | 15.2 KB | ||
| 4. Examining Statistics of Variables.mp4 | 84.3 MB | ||
| 4. Examining Statistics of Variables.srt | 24.6 KB | ||
| 4. Getting to Know the Kaggle Homepage.mp4 | 112.4 MB | ||
| 4. Getting to Know the Kaggle Homepage.srt | 25 KB | ||
| 4. Hyperparameter Optimization (with GridSearchCV).mp4 | 54.7 MB | ||
| 4. Hyperparameter Optimization (with GridSearchCV).srt | 17.4 KB | ||
| 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html | 102.4 B | ||
| 4. What Should Be Done to Achieve Success in Kaggle.mp4 | 55.4 MB | ||
| 4. What Should Be Done to Achieve Success in Kaggle.srt | 11.5 KB | ||
| 5. Dealing with Outliers – Thalach Variable.mp4 | 33.7 MB | ||
| 5. Dealing with Outliers – Thalach Variable.srt | 11.2 KB | ||
| 5. Decision Tree Algorithm.mp4 | 24 MB | ||
| 5. Decision Tree Algorithm.srt | 7.4 KB | ||
| 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 | 26.6 MB | ||
| 5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.srt | 7.1 KB | ||
| 5. Examining the Missing Data According to the Analysis Result.mp4 | 50 MB | ||
| 5. Examining the Missing Data According to the Analysis Result.srt | 13.9 KB | ||
| 5. Examining the Project Topic.mp4 | 71.7 MB | ||
| 5. Examining the Project Topic.srt | 13.9 KB | ||
| 6. Dealing with Outliers – Oldpeak Variable.mp4 | 33.3 MB | ||
| 6. Dealing with Outliers – Oldpeak Variable.srt | 11 KB | ||
| 6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 | 43.9 MB | ||
| 6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.srt | 8.9 KB | ||
| 6. Recognizing Variables In Dataset.mp4 | 115.4 MB | ||
| 6. Recognizing Variables In Dataset.srt | 23.8 KB | ||
| 6. Support Vector Machine Algorithm.mp4 | 22.7 MB | ||
| 6. Support Vector Machine Algorithm.srt | 6.6 KB | ||
| 7. Determining Distributions of Numeric Variables.mp4 | 23.3 MB | ||
| 7. Determining Distributions of Numeric Variables.srt | 6.5 KB | ||
| 7. Feature Scaling with the Robust Scaler Method.mp4 | 32.7 MB | ||
| 7. Feature Scaling with the Robust Scaler Method.srt | 11.7 KB | ||
| 7. Random Forest Algorithm.mp4 | 27.7 MB | ||
| 7. Random Forest Algorithm.srt | 8.4 KB | ||
| 8. Creating a New DataFrame with the Melt() Function.mp4 | 48.8 MB | ||
| 8. Creating a New DataFrame with the Melt() Function.srt | 15.1 KB | ||
| 8. Hyperparameter Optimization (with GridSearchCV).mp4 | 48.6 MB | ||
| 8. Hyperparameter Optimization (with GridSearchCV).srt | 14.4 KB | ||
| 8. Transformation Operations on Unsymmetrical Data.mp4 | 22.2 MB | ||
| 8. Transformation Operations on Unsymmetrical Data.srt | 6.3 KB | ||
| 9. Applying One Hot Encoding Method to Categorical Variables.mp4 | 22.4 MB | ||
| 9. Applying One Hot Encoding Method to Categorical Variables.srt | 7.6 KB | ||
| 9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 | 39.2 MB | ||
| 9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.srt | 8.3 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 140 total files | |||
Kaggle Master with Heart Attack Prediction Kaggle Project
https://DevCourseWeb.com
Published 05/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 71 lectures (11h 2m) | Size: 3.73 GB
Kaggle is Machine Learning & Data Science community. Become Kaggle master with real machine learning kaggle project
What you'll learn
Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.
Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detect
Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and ne
Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithm
Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources
Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems.
What is Kaggle?
Registering on Kaggle and Member Login Procedures
Getting to Know the Kaggle Homepage
Competitions on Kaggle
Datasets on Kaggle
Examining the Code Section in Kaggle
What is Discussion on Kaggle?
Courses in Kaggle
Ranking Among Users on Kaggle
Blog and Documentation Sections
User Page Review on Kaggle
Treasure in The Kaggle
Publishing Notebooks on Kaggle
What Should Be Done to Achieve Success in Kaggle?
First Step to the Project
Notebook Design to be Used in the Project
Examining the Project Topic
Recognizing Variables in Dataset
Required Python Libraries
Loading the Dataset
Initial analysis on the dataset
Examining Missing Values
Examining Unique Values
Separating variables (Numeric or Categorical)
Examining Statistics of Variables
Numeric Variables (Analysis with Distplot)
Categoric Variables (Analysis with Pie Chart)
Examining the Missing Data According to the Analysis Result
Numeric Variables – Target Variable (Analysis with FacetGrid)
Categoric Variables – Target Variable (Analysis with Count Plot)
Examining Numeric Variables Among Themselves (Analysis with Pair Plot)
Feature Scaling with the Robust Scaler Method for New Visualization
Creating a New DataFrame with the Melt() Function
Numerical - Categorical Variables (Analysis with Swarm Plot)
Numerical - Categorical Variables (Analysis with Box Plot)
Relationships between variables (Analysis with Heatmap)
Dropping Columns with Low Correlation
Visualizing Outliers
Dealing with Outliers
Determining Distributions of Numeric Variables
Transformation Operations on Unsymmetrical Data
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms
Separating Data into Test and Training Set
Logistic Regression
Cross Validation for Logistic Regression Algorithm
Roc Curve and Area Under Curve (AUC) for Logistic Regression Algorithm
Hyperparameter Optimization (with GridSearchCV) for Logistic Regression Algorithm
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Hyperparameter Optimization (with GridSearchCV) for Random Forest Algorithm
Project Conclusion and Sharing
Requirements
Desire to learn about Kaggle
Watch the course videos completely and in order
Internet Connection.
Any device such as mobile phone, computer, or tablet where you can watch the lesson.
Learning determination and patience.
LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device
Nothing else! It’s just you, your computer and your ambition to get started today
Desire to improve Data Science, Machine Learning, Python Portfolio with Kaggle
Free software and tools used during the course
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 9.01 GB | Neozoon | 2 years | 11 | 0 | |
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Udemy - Kaggle - Get The Best Data Science, Machine Learning Profile Posted by
freecoursewb in Other
|
1.5 GB | freecoursewb | 4 years | 0 | 0 |
| 1.1 GB | freecoursewb | 4 years | 0 | 0 | |
| 1.4 GB | tutsnode | 5 years | 10 | 1 | |
| 4.6 GB | tutsgalaxy | 6 years | 10 | 6 |
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