Generative Ai For Data Analysis And Engineering With Chatgpt

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Generative Ai For Data Analysis And Engineering With Chatgpt - BaDshaH - 10-06-2024

[Image: 64b1ec210f7e1d01477a7688392ad325.jpg]

Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.22 GB | Duration: 12h 7m

ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

What you'll learn
Data analysis is the process of studying or manipulating a dataset to gain some sort of insight
Big News: Introducing ChatGPT-4o
How to Use ChatGPT-4o?
Chronological Development of ChatGPT
What Are the Capabilities of ChatGPT-4o?
As an App: ChatGPT
Voice Communication with ChatGPT-4o
Instant Translation in 50+ Languages
Interview Preparation with ChatGPT-4o
Visual Commentary with ChatGPT-4o
ChatGPT for Generative AI Introduction
Accessing the Dataset
First Task: Field Knowledge
Continuing with Field Knowledge
Loading the Dataset and Understanding Variables
Delving into the Details of Variables
Let's Perform the First Analysis
Updating Variable Names
Examining Missing Values
Examining Unique Values
Examining Statistics of Variables
Exploratory Data Analysis (EDA)
Categorical Variables (Analysis with Pie Chart)
Importance of Bivariate Analysis in Data Science
Numerical Variables vs Target Variable
Categoric Variables vs Target Variable
Correlation Between Numerical and Categorical Variables and the Target Variable
Examining Numeric Variables Among Themselves
Numerical Variables - Categorical Variables
Numerical Variables - Categorical Variables with Swarm Plot
Relationships between variables (Analysis with Heatmap)
Preparation for Modeling
Dropping Columns with Low Correlation
Struggling Outliers
Visualizing Outliers
Dealing with Outliers
Determining Distributions
Determining Distributions of Numeric Variables
Applying One Hot Encoding Method to Categorical Variables
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
Logistic Regression Algorithm
Cross Validation
ROC Curve and Area Under Curve (AUC)
ROC Curve and Area Under Curve (AUC)
Hyperparameter Tuning for Logistic Regression Model
Decision Tree Algorithm
Support Vector Machine Algorithm
Random Forest Algorithm
Generative AI is artificial intelligence (AI) that can create original content in response to a user's prompt or request

Requirements
A working computer (Windows, Mac, or Linux)
Motivation to learn the the second largest number of job postings relative AI among all others
Desire to learn Generative AI & ChatGPT
Curiosity for Artificial Intelligence and Data Science
Basic python knowledge
Nothing else! It's just you, your computer and your ambition to get started today

Description
Hi there,Welcome to "Generative AI for Data Analysis and Engineering with ChatGPT" course.ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data AnalystArtificial Intelligence (AI) is transforming the way we interact with technology, and mastering AI tools has become essential for anyone looking to stay ahead in the digital age. In today's data-driven world, the ability to analyze data, draw meaningful insights, and apply machine learning algorithms is more crucial than ever. This course is designed to guide you through every step of that journey, from the basics of Exploratory Data Analysis (EDA) to mastering advanced machine learning algorithms, all while leveraging the power of ChatGPT-4o.What This Course Offers:In this course, you will gain a deep understanding of the entire data analysis and machine learning pipeline. Whether you are new to the field or looking to expand your existing knowledge, our hands-on approach will equip you with the skills you need to tackle real-world data challenges.You'll begin by diving into the fundamentals of EDA, where you'll learn how to explore, visualize, and interpret datasets. With step-by-step guidance, you'll master techniques to clean, transform, and analyze data to uncover trends, patterns, and outliers-key steps before jumping into predictive modeling.Why ChatGPT-4o?This course uniquely integrates ChatGPT-4o, the next-gen AI tool, to assist you throughout your learning journey. ChatGPT-4o will enhance your productivity by automating tasks, helping with code generation, answering queries, and offering suggestions for better analysis and model optimization. You'll see how this cutting-edge AI transforms data analysis workflows and unlocks new levels of efficiency and creativity.Mastering Machine Learning:Once your foundation in EDA is solid, the course will guide you through advanced machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and more. You'll learn not only how these algorithms work but also how to implement and optimize them using real-world datasets. By the end of the course, you'll be proficient in selecting the right models, fine-tuning hyperparameters, and evaluating model performance with confidence.What You'll Learn:Exploratory Data Analysis (EDA): Master the techniques for analyzing and visualizing data, detecting trends, and preparing data for modeling.Machine Learning Algorithms: Implement algorithms like Logistic Regression, Decision Trees, and Random Forest, and understand when and how to use them.ChatGPT-4o Integration: Leverage the AI capabilities of ChatGPT-4o to automate workflows, generate code, and improve data insights.Real-World Applications: Apply the knowledge gained to solve complex problems and make data-driven decisions in industries such as finance, healthcare, and technology.Next-Gen AI Techniques: Explore advanced techniques that combine AI with machine learning, pushing the boundaries of data analysis.Why This Course Stands Out:Unlike traditional data science courses, this course blends theory with practice. You won't just learn how to perform data analysis or build machine learning models-you'll also apply these skills in real-world scenarios with guidance from ChatGPT-4o. The hands-on projects ensure that by the end of the course, you can confidently take on any data challenge in your professional career.In this course, you will Learn:Big News: Introducing ChatGPT-4oHow to Use ChatGPT-4o?Chronological Development of ChatGPTWhat Are the Capabilities of ChatGPT-4o?As an App: ChatGPTVoice Communication with ChatGPT-4oInstant Translation in 50+ LanguagesInterview Preparation with ChatGPT-4oVisual Commentary with ChatGPT-4oChatGPT for Generative AI IntroductionAccessing the DatasetFirst Task: Field KnowledgeContinuing with Field KnowledgeLoading the Dataset and Understanding VariablesDelving into the Details of VariablesLet's Perform the First AnalysisUpdating Variable NamesExamining Missing ValuesExamining Unique ValuesExamining Statistics of VariablesExploratory Data Analysis (EDA)Categorical Variables (Analysis with Pie Chart)Importance of Bivariate Analysis in Data ScienceNumerical Variables vs Target VariableCategoric Variables vs Target VariableCorrelation Between Numerical and Categorical Variables and the Target VariableExamining Numeric Variables Among ThemselvesNumerical Variables - Categorical VariablesNumerical Variables - Categorical Variables with Swarm PlotRelationships between variables (Analysis with Heatmap)Preparation for ModelingDropping Columns with Low CorrelationStruggling OutliersVisualizing OutliersDealing with OutliersDetermining DistributionsDetermining Distributions of Numeric VariablesApplying One Hot Encoding Method to Categorical VariablesFeature Scaling with the RobustScaler Method for Machine Learning AlgorithmsSeparating Data into Test and Training SetLogistic Regression AlgorithmCross ValidationROC Curve and Area Under Curve (AUC)Hyperparameter Optimization (with GridSearchCV)Hyperparameter Tuning for Logistic Regression ModelDecision Tree AlgorithmSupport Vector Machine AlgorithmRandom Forest AlgorithmSummaryBeginners who want a structured, comprehensive introduction to data analysis and machine learning.Data enthusiasts looking to enhance their AI-driven analysis and modeling skills.Professionals who want to integrate AI tools like ChatGPT-4o into their data workflows.Anyone interested in mastering the art of data analysis, machine learning, and next-generation AI techniques.What You'll Gain:By the end of this course, you will have a robust toolkit that enables you to:Transform raw data into actionable insights with EDA.Build, evaluate, and fine-tune machine learning models with confidence.Use ChatGPT-4o to streamline data analysis, automate repetitive tasks, and generate faster results.Apply advanced AI techniques to tackle industry-level problems and make data-driven decisions.This course is your gateway to mastering data analysis, machine learning, and AI, and it's designed to provide you with both the theoretical knowledge and practical skills needed to succeed in today's data-centric world.Join us on this complete journey and unlock the full potential of data with ChatGPT-4o and advanced machine learning algorithms. Let's get started!Video and Audio Production QualityAll our videos are created/produced as high-quality video and audio to provide you the best learning experience.You will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadDive in now!We offer full support, answering any questions.See you in the "Generative AI for Data Analysis and Engineering with ChatGPT" course.ChatGPT and AI | Data Analytics and ML Mastering Course with ChatGPT-4o and Next-Gen AI Techniques for Data Analyst

Overview
Section 1: Project Files and Sources
Lecture 1 The Main Prompt Source of The Course
Lecture 2 Prompts
Lecture 3 Github Link
Lecture 4 Kaggle Link
Section 2: ChatGPT-4o Unleashed: Innovations in Communication and Learning
Lecture 5 Big News: Introducing ChatGPT-4o
Lecture 6 How to Use ChatGPT-4o?
Lecture 7 Chronological Development of ChatGPT
Lecture 8 What Are the Capabilities of ChatGPT-4o?
Lecture 9 As an App: ChatGPT
Lecture 10 Voice Communication with ChatGPT-4o
Lecture 11 Instant Translation in 50+ Languages
Lecture 12 Interview Preparation with ChatGPT-4o
Lecture 13 Visual Commentary with ChatGPT-4o: Lesson 1
Lecture 14 Visual Commentary with ChatGPT-4o: Lesson 2
Section 3: Dataset Exploration and Field Knowledge
Lecture 15 ChatGPT for Generative AI Introduction
Lecture 16 Accessing the Dataset
Lecture 17 First Task: Field Knowledge
Lecture 18 Continuing with Field Knowledge
Lecture 19 Loading the Dataset and Understanding Variables
Lecture 20 Delving into the Details of Variables
Section 4: Variable Analysis: Missing Data, Unique Values, and Statistics
Lecture 21 Let's Perform the First Analysis
Lecture 22 Updating Variable Names
Lecture 23 Examining Missing Values
Lecture 24 Examining Unique Values
Lecture 25 Examining Statistics of Variables Lesson 1
Lecture 26 Examining Statistics of Variables Lesson 2
Lecture 27 Examining Statistics of Variables Lesson 3
Section 5: Exploratory Data Analysis (EDA) 1
Lecture 28 Exploratory Data Analysis (EDA)
Lecture 29 Categorical Variables (Analysis with Pie Chart) Lesson 1
Lecture 30 Categorical Variables (Analysis with Pie Chart) Lesson 2
Lecture 31 Categorical Variables (Analysis with Pie Chart) Lesson 3
Lecture 32 Categorical Variables (Analysis with Pie Chart) Lesson 4
Lecture 33 Categorical Variables (Analysis with Pie Chart) Lesson 5
Section 6: Exploratory Data Analysis (EDA) 2
Lecture 34 Importance of Bivariate Analysis in Data Science
Lecture 35 Numerical Variables vs Target Variable Lesson 1
Lecture 36 Numerical Variables vs Target Variable Lesson 2
Lecture 37 Numerical Variables vs Target Variable Lesson 3
Lecture 38 Numerical Variables vs Target Variable Lesson 4
Lecture 39 Categoric Variables vs Target Variable Lesson 1
Lecture 40 Categoric Variables vs Target Variable Lesson 2
Lecture 41 Categoric Variables vs Target Variable Lesson 3
Lecture 42 Categoric Variables vs Target Variable Lesson 4
Lecture 43 Categoric Variables vs Target Variable Lesson 5
Section 7: Exploratory Data Analysis (EDA) 3
Lecture 44 Correlation Between Numerical and Categorical Variables and the Target Variable
Lecture 45 Correlation Between Numerical and Categorical Variables and the Target Variable
Lecture 46 Examining Numeric Variables Among Themselves Lesson 1
Lecture 47 Examining Numeric Variables Among Themselves Lesson 2
Lecture 48 Numerical Variables - Categorical Variables Lesson 1
Lecture 49 Numerical Variables - Categorical Variables Lesson 2
Lecture 50 Numerical Variables - Categorical Variables Lesson 3
Lecture 51 Numerical Variables - Categorical Variables Lesson 4
Lecture 52 Numerical Variables - Categorical Variables Lesson 5
Lecture 53 Numerical Variables - Categorical Variables with Swarm Plot Lesson 1
Lecture 54 Numerical Variables - Categorical Variables with Swarm Plot Lesson 2
Lecture 55 Numerical Variables - Categorical Variables with Swarm Plot Lesson 3
Lecture 56 Numerical Variables - Categorical Variables with Swarm Plot Lesson 4
Lecture 57 Numerical Variables - Categorical Variables with Swarm Plot Lesson 5
Lecture 58 Numerical Variables - Categorical Variables with Swarm Plot Lesson 6
Lecture 59 Relationships between variables (Analysis with Heatmap) Lesson 1
Lecture 60 Relationships between variables (Analysis with Heatmap) Lesson 2
Section 8: Preparation for Modeling
Lecture 61 Preparation for Modeling
Lecture 62 Dropping Columns with Low Correlation
Lecture 63 Struggling Outliers
Lecture 64 Visualizing Outliers Lesson 1
Lecture 65 Visualizing Outliers Lesson 2
Lecture 66 Visualizing Outliers Lesson 3
Lecture 67 Dealing with Outliers Lesson 1
Lecture 68 Dealing with Outliers Lesson 2
Lecture 69 Dealing with Outliers Lesson 3
Lecture 70 Dealing with Outliers Lesson 4
Lecture 71 Dealing with Outliers Lesson 5
Lecture 72 Determining Distributions
Lecture 73 Determining Distributions of Numeric Variables Lesson 1
Lecture 74 Determining Distributions of Numeric Variables Lesson 2
Lecture 75 Determining Distributions of Numeric Variables Lesson 3
Lecture 76 Determining Distributions of Numeric Variables Lesson 4
Lecture 77 Determining Distributions of Numeric Variables Lesson 5
Lecture 78 Applying One Hot Encoding Method to Categorical Variables Lesson
Lecture 79 Applying One Hot Encoding Method to Categorical Variables Lesson
Lecture 80 Feature Scaling with the RobustScaler Method for Machine Learning Algorithms
Lecture 81 Separating Data into Test and Training Set
Section 9: Machine Learning Algorithm - Logistic Regression
Lecture 82 Logistic Regression Algorithm Lesson 1
Lecture 83 Logistic Regression Algorithm Lesson 2
Lecture 84 Cross Validation
Lecture 85 ROC Curve and Area Under Curve (AUC) Lesson 1
Lecture 86 ROC Curve and Area Under Curve (AUC) Lesson 2
Lecture 87 Hyperparameter Optimization (with GridSearchCV)
Lecture 88 Hyperparameter Tuning for Logistic Regression Model
Section 10: Machine Learning Algorithm - Decision Tree & SVM
Lecture 89 Decision Tree Algorithm Lesson 1
Lecture 90 Decision Tree Algorithm Lesson 2
Lecture 91 Support Vector Machine Algorithm Lesson 1
Lecture 92 Support Vector Machine Algorithm Lesson 2
Section 11: Machine Learning Algorithm - Random Forest
Lecture 93 Random Forest Algorithm Lesson 1
Lecture 94 Random Forest Algorithm Lesson 2
Lecture 95 Random Forest Algorithm Lesson 3
Lecture 96 Random Forest Algorithm Lesson 4
Section 12: Conclusion
Lecture 97 Project Conclusion
Lecture 98 Suggestions and Closing
Section 13: Extra
Lecture 99 Generative AI for Data Analysis and Engineering with ChatGPT
Anyone who wants to start learning AI & ChatGPT,Anyone who needs a complete guide on how to start and continue their career with AI & Prompt Engineering,And also, who want to learn how to develop Prompt Engineering,Data Analyst who want to apply generative AI tools to automate repetitive tasks, streamline data workflows, and generate insights.,Data Engineer who wants to optimize data pipelines and automate data-related tasks.,AI and Machine Learning Enthusiasts who want to deepen their understanding of how generative AI models, like ChatGPT, can be applied to real-world data tasks.,Business Analysts who wants to understand how generative AI can assist in generating business insights from raw data,Students or Beginners in Data Science who want to get familiar with cutting-edge AI tools and apply them to basic data analysis, engineering, or project automation.

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