Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project

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Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project (Size: 3.6 GB)
  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

Description


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

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