Download Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project torrent

Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project

Torrent:	Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project
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
Downloads:	39
Category:	Other /Tutorials
Size:	3.6 GB
Show Files »	[ FreeCourseWeb.com ] Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. First Contact with Kaggle 1. What is Kaggle.mp4 (122.4 MB) 1. What is Kaggle.srt (23.0 KB) 2. FAQ about Kaggle.html (10.9 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) 4. Getting to Know the Kaggle Homepage.mp4 (112.4 MB) 4. Getting to Know the Kaggle Homepage.srt (25.0 KB) 10. Preparation For Exploratory Data Analysis (EDA) in Data Science 1. Examining Missing Values.mp4 (42.4 MB) 1. Examining Missing Values.srt (13.2 KB) 2. Examining Unique Values.mp4 (41.0 MB) 2. Examining Unique Values.srt (12.9 KB) 3. Separating variables (Numeric or Categorical).mp4 (14.7 MB) 3. Separating variables (Numeric or Categorical).srt (4.6 KB) 4. Examining Statistics of Variables.mp4 (84.3 MB) 4. Examining Statistics of Variables.srt (24.6 KB) 11. Exploratory Data Analysis (EDA) - Uni-variate Analysis 1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 (74.6 MB) 1. Numeric Variables (Analysis with Distplot) Lesson 1.srt (20.2 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) 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 (69.0 MB) 3. Categoric Variables (Analysis with Pie Chart) Lesson 1.srt (19.7 KB) 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 (78.0 MB) 4. Categoric Variables (Analysis with Pie Chart) Lesson 2.srt (20.9 KB) 5. Examining the Missing Data According to the Analysis Result.mp4 (50.0 MB) 5. Examining the Missing Data According to the Analysis Result.srt (13.9 KB) 12. Exploratory Data Analysis (EDA) - Bi-variate Analysis 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) 10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 (64.0 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 (36.0 MB) 11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.srt (10.1 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 (16.0 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) 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 (5.0 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) 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) 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) 7. Feature Scaling with the Robust Scaler Method.mp4 (32.7 MB) 7. Feature Scaling with the Robust Scaler Method.srt (11.7 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) 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) 13. Preparation for Modelling in Machine Learning 1. Dropping Columns with Low Correlation.mp4 (24.8 MB) 1. Dropping Columns with Low Correlation.srt (5.2 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) 11. Separating Data into Test and Training Set.mp4 (27.8 MB) 11. Separating Data into Test and Training Set.srt (9.4 KB) 2. Visualizing Outliers.mp4 (32.7 MB) 2. Visualizing Outliers.srt (11.9 KB) 3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 (40.0 MB) 3. Dealing with Outliers – Trtbps Variable Lesson 1.srt (13.7 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) 5. Dealing with Outliers – Thalach Variable.mp4 (33.7 MB) 5. Dealing with Outliers – Thalach Variable.srt (11.2 KB) 6. Dealing with Outliers – Oldpeak Variable.mp4 (33.3 MB) 6. Dealing with Outliers – Oldpeak Variable.srt (11.0 KB) 7. Determining Distributions of Numeric Variables.mp4 (23.3 MB) 7. Determining Distributions of Numeric Variables.srt (6.5 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) 14. Modelling for Machine Learning 1. Logistic Regression.mp4 (27.3 MB) 1. Logistic Regression.srt (9.1 KB) 2. Cross Validation.mp4 (28.2 MB) 2. Cross Validation.srt (7.6 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) 4. Hyperparameter Optimization (with GridSearchCV).mp4 (54.7 MB) 4. Hyperparameter Optimization (with GridSe files
Added:	2022-05-27 10:04:15
Language:	English
Peers:	Seeders : 9 , Leechers : 2
Tags:	Development Data Science Udemy
Release name:	Udemy - Kaggle Master with Heart Attack Prediction Kaggle Project
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