Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Generative AI for Data Analysis and Engineering with ChatGPT
Project Files and Sources
The Main Prompt Source of The Course
Prompts
Github Link
Kaggle Link
ChatGPT-4o Unleashed: Innovations in Communication and Learning
How to Use ChatGPT-4o? (5:53)
Big News: Introducing ChatGPT-4o (4:54)
Chronological Development of ChatGPT (5:21)
What Are the Capabilities of ChatGPT-4o? (4:33)
As an App: ChatGPT (3:18)
Voice Communication with ChatGPT-4o (4:50)
Instant Translation in 50+ Languages (3:03)
Interview Preparation with ChatGPT-4o (18:06)
Visual Commentary with ChatGPT-4o: Lesson 1 (4:19)
Visual Commentary with ChatGPT-4o: Lesson 2 (4:56)
Dataset Exploration and Field Knowledge
ChatGPT for Generative AI Introduction (4:21)
Accessing the Dataset (1:35)
First Task: Field Knowledge (11:12)
Continuing with Field Knowledge (5:42)
Loading the Dataset and Understanding Variables (7:55)
Delving into the Details of Variables (5:35)
Variable Analysis: Missing Data, Unique Values, and Statistics
Updating Variable Names (6:19)
Let's Perform the First Analysis (6:37)
Examining Missing Values (6:07)
Examining Unique Values (14:12)
Examining Statistics of Variables Lesson 1 (15:15)
Examining Statistics of Variables Lesson 2 (13:11)
Examining Statistics of Variables Lesson 3 (9:19)
Exploratory Data Analysis (EDA) 1
Categorical Variables (Analysis with Pie Chart) Lesson 1 (10:40)
Exploratory Data Analysis (EDA) (9:59)
Categorical Variables (Analysis with Pie Chart) Lesson 2 (9:35)
Categorical Variables (Analysis with Pie Chart) Lesson 3 (6:52)
Categorical Variables (Analysis with Pie Chart) Lesson 4 (16:44)
Categorical Variables (Analysis with Pie Chart) Lesson 5 (11:19)
Exploratory Data Analysis (EDA) 2
Numerical Variables vs Target Variable Lesson 1 (6:42)
Importance of Bivariate Analysis in Data Science (7:16)
Numerical Variables vs Target Variable Lesson 2 (9:56)
Numerical Variables vs Target Variable Lesson 3 (8:24)
Numerical Variables vs Target Variable Lesson 4 (3:36)
Categoric Variables vs Target Variable Lesson 1 (3:37)
Categoric Variables vs Target Variable Lesson 2 (5:31)
Categoric Variables vs Target Variable Lesson 3 (5:21)
Categoric Variables vs Target Variable Lesson 4 (4:45)
Categoric Variables vs Target Variable Lesson 5 (5:51)
Exploratory Data Analysis (EDA) 3
Correlation Between Numerical and Categorical Variables and the Target Variable (11:42)
Correlation Between Numerical and Categorical Variables and the Target Variable (7:57)
Examining Numeric Variables Among Themselves Lesson 1 (6:12)
Examining Numeric Variables Among Themselves Lesson 2 (6:56)
Numerical Variables - Categorical Variables Lesson 1 (18:12)
Numerical Variables - Categorical Variables Lesson 2 (6:01)
Numerical Variables - Categorical Variables Lesson 3 (5:21)
Numerical Variables - Categorical Variables Lesson 4 (5:17)
Numerical Variables - Categorical Variables Lesson 5 (5:48)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 1 (12:33)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 2 (7:19)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 3 (7:01)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 4 (4:35)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 5 (4:31)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 6 (8:48)
Relationships between variables (Analysis with Heatmap) Lesson 1 (8:07)
Relationships between variables (Analysis with Heatmap) Lesson 2 (14:44)
Preparation for Modeling
Dropping Columns with Low Correlation (5:26)
Preparation for Modeling (5:23)
Struggling Outliers (9:50)
Visualizing Outliers Lesson 1 (6:30)
Visualizing Outliers Lesson 2 (4:33)
Visualizing Outliers Lesson 3 (3:51)
Dealing with Outliers Lesson 1 (9:24)
Dealing with Outliers Lesson 2 (13:50)
Dealing with Outliers Lesson 3 (6:02)
Dealing with Outliers Lesson 4 (6:38)
Dealing with Outliers Lesson 5 (10:03)
Determining Distributions (11:24)
Determining Distributions of Numeric Variables Lesson 1 (6:26)
Determining Distributions of Numeric Variables Lesson 2 (4:06)
Determining Distributions of Numeric Variables Lesson 3 (4:30)
Determining Distributions of Numeric Variables Lesson 4 (8:20)
Determining Distributions of Numeric Variables Lesson 5 (6:53)
Applying One Hot Encoding Method to Categorical Variables Lesson (5:19)
Applying One Hot Encoding Method to Categorical Variables Lesson 2 (2:31)
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms (3:44)
Separating Data into Test and Training Set (4:00)
Machine Learning Algorithm – Logistic Regression
Logistic Regression Algorithm Lesson 1 (6:14)
Logistic Regression Algorithm Lesson 1 (11:15)
Cross Validation (8:44)
ROC Curve and Area Under Curve (AUC) Lesson 1 (6:56)
ROC Curve and Area Under Curve (AUC) Lesson 2 (5:48)
Hyperparameter Optimization (with GridSearchCV) (7:33)
Hyperparameter Tuning for Logistic Regression Model (8:28)
Machine Learning Algorithm – Decision Tree & SVM
Decision Tree Algorithm Lesson 1 (5:50)
Decision Tree Algorithm Lesson 2 (6:55)
Support Vector Machine Algorithm Lesson 1 (5:34)
Support Vector Machine Algorithm Lesson 2 (6:23)
Machine Learning Algorithm – Random Forest
Random Forest Algorithm Lesson 2 (3:37)
Random Forest Algorithm Lesson 1 (6:09)
Random Forest Algorithm Lesson 3 (5:03)
Random Forest Algorithm Lesson 4 (5:43)
Conclusion
Project Conclusion (10:35)
Suggestions and Closing (8:07)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 4
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock