Download Udemy - Machine Learning Project - Heart Attack Prediction Analysis torrent

Udemy - Machine Learning Project - Heart Attack Prediction Analysis

Torrent:	Udemy - Machine Learning Project - Heart Attack Prediction Analysis
Description:	Machine Learning Project: Heart Attack Prediction Analysis https://DevCourseWeb.com Published 05/2022 MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning \| Language: English + srt \| Duration: 53 lectures (7h 23m) \| Size: 2.2 GB Data Science & Machine Learning - Boost your Machine Learning, statistics skills with real heart attack analysis project What you'll learn 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. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. 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 Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. 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 master on machine learning a-z, python, data science, statistics Knowledge of Python Programming Language Knowledge of data visualization libraries like Seaborn, Matplotlib in Python Knowledge of basic Machine Learning Be Able to Operate & Install Software On A Computer Free software and tools used during the course Determination to learn and patience.
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Category:	Other /Tutorials
Size:	2.1 GB
Show Files »	[ FreeCourseWeb.com ] Udemy - Machine Learning Project - Heart Attack Prediction Analysis Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Introduction to Machine Learning with Real Hearth Attack Prediction Project 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) 2. FAQ about Machine Learning, Data Science.html (15.3 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) 4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html (0.1 KB) 5. Examining the Project Topic.mp4 (71.7 MB) 5. Examining the Project Topic.srt (13.9 KB) 6. Recognizing Variables In Dataset.mp4 (115.3 MB) 6. Recognizing Variables In Dataset.srt (23.8 KB) 2. First Organization 1. Required Python Libraries.mp4 (58.8 MB) 1. Required Python Libraries.srt (12.8 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) 3. Initial analysis on the dataset.mp4 (58.7 MB) 3. Initial analysis on the dataset.srt (18.2 KB) 3. 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) 4. 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) 5. Exploratory Data Analysis (EDA) - Bi-variate Analysis 1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 (45.3 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 (32.6 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 (39.3 MB) 9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.srt (8.3 KB) 6. 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. Transform files
Added:	2022-05-27 10:04:20
Language:	English
Peers:	Seeders : 11 , Leechers : 3
Tags:	Development Data Science Udemy
Release name:	Udemy - Machine Learning Project - Heart Attack Prediction Analysis
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