Generative Ai & Chatgpt Mastery For Data Science And Python

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Generative Ai & Chatgpt Mastery For Data Science And Python
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.12 GB | Duration: 24h 6m

Master Generative AI, ChatGPT and Prompt Engineering for Data Science and Python from scratch with hands-on projects

[b]What you'll learn[/b]

What is Artificial Intelligence?

Artificial Narrow Intelligence (ANI)

Artificial General Intelligence (AGI)

Artificial Super Intelligence (ASI)

Subsets of Artificial Intelligence - Machine Learning

Subsets of Artificial Intelligence - Deep Learning

Machine Learning Study with a Real Example

Large Language Models(LLM)

Natural Language Processing(NLP)

A Warning Before Switching to ChatGPT

Revolutionary of the Era: OpenAI

Let's Get to Know the ChatGPT Interface

Differences in the ChatGPT-4 Interface

ChatGPT's Endpoints

Prompt Prompt Engineering Power

Summary of Prompt Engineering Fundamentals

Prompt Engineering: Sample Prompts

Best Questions in Prompt Engineering

Summary of the Best Questions in Prompt Engineering

Reinforcing the topic through a scenario

Drawing a Roadmap to the Prompt

Directed Writing Request

Clear Explanation Method

Example-Based Learning

RGC(Role, Goals, Context)

Constrained Responses

Adding Visual Appeal

Prompt Updates

ChatGPT-Google Extension

Email Writing

Summarizing YouTube Videos

Talk to ChatGPT

Quick Access to ChatGPT

Dive Into Websites

Get Prompt Assistance

Using the ChatGPT API

File Reading

Visual Reading

Visual Generation (DALL-E Introduction)

Enhancing Images with DALL-E

Improving Visuals Through Ready-Made Prompts

Combining Images

A Helper Site for Visual Prompts

GPTs

Create Your Own GPT

Useful GPTs

Big News: Introducing ChatGPT-4o

How to Use ChatGPT-4o?

Chronological Development of ChatGPT

What Are the Capabilities of ChatGPT-4o?

Voice Communication with ChatGPT-4o

Instant Translation in 50+ Languages

Interview Preparation with ChatGPT-4o

Visual Commentary with ChatGPT-4o

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

Loading the Dataset and Understanding Variables

Let's Perform the First Analysis

Examining Missing Values

Examining Unique Values

Categorical Variables (Analysis with Pie Chart)

Exploratory Data Analysis (EDA)

Categoric Variables vs Target Variable

Correlation Between Numerical and Categorical Variables and the Target Variable

Relationships between variables (Analysis with Heatmap)

Numerical Variables - Categorical Variables with Swarm Plot

Dropping Columns with Low Correlation

Visualizing Outliers

Determining Distributions

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

Getting to know the dataset using ChatGPT

Getting started with Exploratory Data Analysis(EDA) using ChatGPT

Perform Multivariate Analysis using ChatGPT

Prepare data for machine learning model using ChatGPT

Create a machine learning model using the Linear Regression algorithm with ChatGPT

Develop machine learning model using ChatGPT

Perform Feature Engineering using ChatGPT

Performing Hyperparameter Optimization using ChatGPT

Loading Dataset using ChatGPT

Perform initial analysis on Dataset using ChatGPT

Performing the first operation on the Dataset using ChatGPT

Tackling Missing values ​​using ChatGPT

Performing Bivariate analysis with CatPLot using ChatGPT

Performing Bivariate analysis with KdePLot using ChatGPT

Examining the correlation of variables using ChatGPT

Perform a get_dummies operation using ChatGPT

Prepare for Logistic Regression modeling using ChatGPT

Create a Logistic Regression model using ChatGPT

Examining evaluation metrics on the Logistic Regression model using ChatGPT

Perform a GridSearchCv operation using ChatGPT

Model reconstruction with best parameters using ChatGPT

[b]Requirements[/b]

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 AI & ChatGPT

Curiosity for Artificial Intelligence and Data Science

Nothing else! It's just you, your computer and your ambition to get started today

Basic python knowledge

[b]Description[/b]

Hi there,Welcome to "Generative AI & ChatGPT Mastery for Data Science and Python" course.Master Generative AI, ChatGPT and Prompt Engineering for Data Science and Python from scratch with hands-on projectsArtificial 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.Data science application is an in-demand skill in many industries worldwide - including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you're into research, statistics, and analytics.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat.A machine learning course teaches you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python, python programming, python examples, python example, python hands-on, pycharm python, python pycharm, python with examples, python: learn python with real python hands-on examples, learn python, real pythonPython's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.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:What is Artificial Intelligence?Artificial Narrow Intelligence (ANI)Artificial General Intelligence (AGI)Artificial Super Intelligence (ASI)Subsets of Artificial Intelligence - Machine LearningSubsets of Artificial Intelligence - Deep LearningMachine Learning vs. Deep LearningMachine Learning Study with a Real ExampleLarge Language Models(LLM)Natural Language Processing(NLP)A Warning Before Switching to ChatGPTRevolutionary of the Era: OpenAIThe Revolution of the Age: Creating a ChatGPT AccountLet's Get to Know the ChatGPT InterfaceChatGPT: Differences Between VersionsDifferences in the ChatGPT-4 InterfaceChatGPT's EndpointsChatGPT's Secret to More Accurate Answers: PromptPrompt Engineering PowerSummary of Prompt Engineering FundamentalsPrompt Engineering: Sample PromptsBest Questions in Prompt EngineeringSummary of the Best Questions in Prompt EngineeringReinforcing the topic through a scenarioDrawing a Roadmap to the PromptDirected Writing RequestClear Explanation MethodExample-Based LearningRGC(Role, Goals, Context)Constrained ResponsesAdding Visual AppealPrompt UpdatesChatGPT-Google ExtensionEmail WritingSummarizing YouTube VideosTalk to ChatGPTQuick Access to ChatGPTDive Into WebsitesGet Prompt AssistanceUsing the ChatGPT APIFile ReadingVisual ReadingVisual Generation (DALL-E Introduction)Enhancing Images with DALL-EImproving Visuals Through Ready-Made PromptsCombining ImagesA Helper Site for Visual PromptsGPTsCreate Your Own GPTUseful GPTsBig 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-4oGetting to know the dataset using ChatGPTGetting started with Exploratory Data Analysis(EDA) using ChatGPTPerform Univariate Analysis using ChatGPTPerform Bivariate Analysis using ChatGPTPerform Multivariate Analysis using ChatGPTPerform Correlation Analysis using ChatGPTPrepare data for machine learning model using ChatGPTCreate a machine learning model using the Linear Regression algorithm with ChatGPTDevelop machine learning model using ChatGPTPerform Feature Engineering using ChatGPTPerforming Hyperparameter Optimization using ChatGPT2.1 Loading Dataset using ChatGPTPerform initial analysis on Dataset using ChatGPTPerforming the first operation on the Dataset using ChatGPTTackling Missing values ​​using ChatGPTPerforming Bivariate analysis with CatPLot using ChatGPTPerforming Bivariate analysis with KdePLot using ChatGPTExamining the correlation of variables using ChatGPTPerform a get_dummies operation using ChatGPTPrepare for Logistic Regression modeling using ChatGPTCreate a Logistic Regression model using ChatGPTExamining evaluation metrics on the Logistic Regression model using ChatGPTPerform a GridSearchCv operation using ChatGPTModel reconstruction with best parameters using ChatGPTSummaryBeginners 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 & ChatGPT Mastery for Data Science and Python" course.Master Generative AI, ChatGPT and Prompt Engineering for Data Science and Python from scratch with hands-on projects

Overview

Section 1: Artificial Intelligence: Concepts, Subsets, and Real-World Examples

Lecture 1 What is Artificial Intelligence?

Lecture 2 Artificial Narrow Intelligence (ANI)

Lecture 3 Artificial General Intelligence (AGI)

Lecture 4 Artificial Super Intelligence (ASI)

Lecture 5 Subsets of Artificial Intelligence - Machine Learning

Lecture 6 Subsets of Artificial Intelligence - Deep Learning

Lecture 7 Machine Learning vs. Deep Learning

Lecture 8 Machine Learning Study with a Real Example: Lesson 1

Lecture 9 Machine Learning Study with a Real Example: Lesson 2

Lecture 10 Large Language Models(LLM)

Lecture 11 Natural Language Processing(NLP)

Section 2: Exploring ChatGPT: Setup, Versions, and Endpoints

Lecture 12 A Warning Before Switching to ChatGPT

Lecture 13 Revolutionary of the Era: OpenAI

Lecture 14 The Revolution of the Age: Creating a ChatGPT Account

Lecture 15 Let's Get to Know the ChatGPT Interface

Lecture 16 ChatGPT: Differences Between Versions

Lecture 17 Differences in the ChatGPT-4 Interface

Lecture 18 ChatGPT's Endpoints

Section 3: The Art of Prompt Engineering: Techniques and Examples

Lecture 19 ChatGPT's Secret to More Accurate Answers: Prompt

Lecture 20 Prompt Engineering Power: Lesson 1

Lecture 21 Prompt Engineering Power: Lesson 2

Lecture 22 Prompt Engineering Power: Lesson 3

Lecture 23 Prompt Engineering Power: Lesson 4

Lecture 24 Summary of Prompt Engineering Fundamentals

Lecture 25 Prompt Engineering: Sample Prompts

Section 4: Critical Questions in Prompt Engineering: A Deep Dive

Lecture 26 Best Questions in Prompt Engineering: Lesson 1

Lecture 27 Best Questions in Prompt Engineering: Lesson 2

Lecture 28 Best Questions in Prompt Engineering: Lesson 3

Lecture 29 Best Questions in Prompt Engineering: Lesson 4

Lecture 30 Best Questions in Prompt Engineering: Lesson 5

Lecture 31 Summary of the Best Questions in Prompt Engineering

Lecture 32 Reinforcing the topic through a scenario

Section 5: Effective Techniques for Crafting Prompts

Lecture 33 Drawing a Roadmap to the Prompt

Lecture 34 Directed Writing Request

Lecture 35 Clear Explanation Method

Lecture 36 Example-Based Learning

Lecture 37 RGC(Role, Goals, Context)

Section 6: Prompt Strengthening Efforts

Lecture 38 Constrained Responses

Lecture 39 Adding Visual Appeal

Lecture 40 Prompt Updates Lesson 1

Lecture 41 Prompt Updates Lesson 2

Lecture 42 Prompt Updates Lesson 3

Lecture 43 Prompt Updates Lesson 4

Section 7: Useful extensions with ChatGPT

Lecture 44 ChatGPT-Google Extension

Lecture 45 Email Writing

Lecture 46 Summarizing YouTube Videos

Lecture 47 Talk to ChatGPT

Lecture 48 Quick Access to ChatGPT

Lecture 49 Dive Into Websites

Lecture 50 Get Prompt Assistance

Section 8: ChatGPT Capabilities

Lecture 51 Using the ChatGPT API

Lecture 52 File Reading

Lecture 53 Visual Reading

Lecture 54 Visual Generation (DALL-E Introduction)

Lecture 55 Enhancing Images with DALL-E

Lecture 56 Improving Visuals Through Ready-Made Prompts

Lecture 57 Combining Images

Lecture 58 A Helper Site for Visual Prompts

Lecture 59 GPTs

Lecture 60 Create Your Own GPT

Lecture 61 Useful GPTs: Lesson 1

Lecture 62 Useful GPTs: Lesson 2

Lecture 63 Useful GPTs: Lesson 3

Section 9: ChatGPT-4o Unleashed: Innovations in Communication and Learning

Lecture 64 Big News: Introducing ChatGPT-4o

Lecture 65 How to Use ChatGPT-4o?

Lecture 66 Chronological Development of ChatGPT

Lecture 67 What Are the Capabilities of ChatGPT-4o?

Lecture 68 As an App: ChatGPT

Lecture 69 Voice Communication with ChatGPT-4o

Lecture 70 Instant Translation in 50+ Languages

Lecture 71 Interview Preparation with ChatGPT-4o

Lecture 72 Visual Commentary with ChatGPT-4o: Lesson 1

Lecture 73 Visual Commentary with ChatGPT-4o: Lesson 2

Section 10: Project Files and Sources

Lecture 74 Source

Lecture 75 Prompts

Lecture 76 Github Link

Lecture 77 Kaggle Link

Section 11: Dataset Exploration and Field Knowledge

Lecture 78 ChatGPT for Generative AI Introduction

Lecture 79 Accessing the Dataset

Lecture 80 First Task: Field Knowledge

Lecture 81 Continuing with Field Knowledge

Lecture 82 Loading the Dataset and Understanding Variables

Lecture 83 Delving into the Details of Variables

Section 12: Variable Analysis: Missing Data, Unique Values, and Statistics

Lecture 84 Let's Perform the First Analysis

Lecture 85 Updating Variable Names

Lecture 86 Examining Missing Values

Lecture 87 Examining Unique Values

Lecture 88 Examining Statistics of Variables Lesson 1

Lecture 89 Examining Statistics of Variables Lesson 2

Lecture 90 Examining Statistics of Variables Lesson 3

Section 13: Exploratory Data Analysis (EDA) 1

Lecture 91 Exploratory Data Analysis (EDA)

Lecture 92 Categorical Variables (Analysis with Pie Chart) Lesson 1

Lecture 93 Categorical Variables (Analysis with Pie Chart) Lesson 2

Lecture 94 Categorical Variables (Analysis with Pie Chart) Lesson 3

Lecture 95 Categorical Variables (Analysis with Pie Chart) Lesson 4

Lecture 96 Categorical Variables (Analysis with Pie Chart) Lesson 5

Section 14: Exploratory Data Analysis (EDA) 2

Lecture 97 Importance of Bivariate Analysis in Data Science

Lecture 98 Numerical Variables vs Target Variable Lesson 1

Lecture 99 Numerical Variables vs Target Variable Lesson 2

Lecture 100 Numerical Variables vs Target Variable Lesson 3

Lecture 101 Numerical Variables vs Target Variable Lesson 4

Lecture 102 Categoric Variables vs Target Variable Lesson 1

Lecture 103 Categoric Variables vs Target Variable Lesson 2

Lecture 104 Categoric Variables vs Target Variable Lesson 3

Lecture 105 Categoric Variables vs Target Variable Lesson 4

Lecture 106 Categoric Variables vs Target Variable Lesson 5

Section 15: Exploratory Data Analysis (EDA) 3

Lecture 107 Correlation Between Numerical and Categorical Variables and the Target Variable

Lecture 108 Correlation Between Numerical and Categorical Variables and the Target Variable

Lecture 109 Examining Numeric Variables Among Themselves Lesson 1

Lecture 110 Examining Numeric Variables Among Themselves Lesson 2

Lecture 111 Numerical Variables - Categorical Variables Lesson 1

Lecture 112 Numerical Variables - Categorical Variables Lesson 2

Lecture 113 Numerical Variables - Categorical Variables Lesson 3

Lecture 114 Numerical Variables - Categorical Variables Lesson 4

Lecture 115 Numerical Variables - Categorical Variables Lesson 5

Lecture 116 Numerical Variables - Categorical Variables with Swarm Plot Lesson 1

Lecture 117 Numerical Variables - Categorical Variables with Swarm Plot Lesson 2

Lecture 118 Numerical Variables - Categorical Variables with Swarm Plot Lesson 3

Lecture 119 Numerical Variables - Categorical Variables with Swarm Plot Lesson 4

Lecture 120 Numerical Variables - Categorical Variables with Swarm Plot Lesson 5

Lecture 121 Numerical Variables - Categorical Variables with Swarm Plot Lesson 6

Lecture 122 Relationships between variables (Analysis with Heatmap) Lesson 1

Lecture 123 Relationships between variables (Analysis with Heatmap) Lesson 2

Section 16: Preparation for Modeling

Lecture 124 Preparation for Modeling

Lecture 125 Dropping Columns with Low Correlation

Lecture 126 Struggling Outliers

Lecture 127 Visualizing Outliers Lesson 1

Lecture 128 Visualizing Outliers Lesson 2

Lecture 129 Visualizing Outliers Lesson 3

Lecture 130 Dealing with Outliers Lesson 1

Lecture 131 Dealing with Outliers Lesson 2

Lecture 132 Dealing with Outliers Lesson 3

Lecture 133 Dealing with Outliers Lesson 4

Lecture 134 Dealing with Outliers Lesson 5

Lecture 135 Determining Distributions

Lecture 136 Determining Distributions of Numeric Variables Lesson 1

Lecture 137 Determining Distributions of Numeric Variables Lesson 2

Lecture 138 Determining Distributions of Numeric Variables Lesson 3

Lecture 139 Determining Distributions of Numeric Variables Lesson 4

Lecture 140 Determining Distributions of Numeric Variables Lesson 5

Lecture 141 Applying One Hot Encoding Method to Categorical Variables Lesson

Lecture 142 Applying One Hot Encoding Method to Categorical Variables Lesson 2

Lecture 143 Feature Scaling with the RobustScaler Method for Machine Learning Algorithms

Lecture 144 Separating Data into Test and Training Set

Section 17: Machine Learning Algorithm - Logistic Regression

Lecture 145 Logistic Regression Algorithm Lesson 1

Lecture 146 Logistic Regression Algorithm Lesson 2

Lecture 147 Cross Validation

Lecture 148 ROC Curve and Area Under Curve (AUC) Lesson 1

Lecture 149 ROC Curve and Area Under Curve (AUC) Lesson 2

Lecture 150 Hyperparameter Optimization (with GridSearchCV)

Lecture 151 Hyperparameter Tuning for Logistic Regression Model

Section 18: Machine Learning Algorithm - Decision Tree & SVM

Lecture 152 Decision Tree Algorithm Lesson 1

Lecture 153 Decision Tree Algorithm Lesson 2

Lecture 154 Support Vector Machine Algorithm Lesson 1

Lecture 155 Support Vector Machine Algorithm Lesson 2

Section 19: Machine Learning Algorithm - Random Forest

Lecture 156 Random Forest Algorithm Lesson 1

Lecture 157 Random Forest Algorithm Lesson 2

Lecture 158 Random Forest Algorithm Lesson 3

Lecture 159 Random Forest Algorithm Lesson 4

Section 20: Conclusion

Lecture 160 Project Conclusion

Lecture 161 Suggestions and Closing

Section 21: Installations

Lecture 162 Installing Anaconda Distribution for Windows

Lecture 163 Installing Anaconda Distribution for MacOs

Lecture 164 Installing Anaconda Distribution for Linux

Lecture 165 Reviewing The Jupyter Notebook

Lecture 166 Reviewing The Jupyter Lab

Section 22: Section 1: Linear Regression Algorithm with ChatGPT

Lecture 167 Getting to know the dataset using ChatGPT

Lecture 168 Getting started with Exploratory Data Analysis(EDA)

Lecture 169 Perform Univariate Analysis using ChatGPT: Lesson 1

Lecture 170 Perform Univariate Analysis using ChatGPT: Lesson 2

Lecture 171 Perform Bivariate Analysis using ChatGPT

Lecture 172 Perform Multivariate Analysis using ChatGPT

Lecture 173 Perform Correlation Analysis using ChatGPT

Lecture 174 Prepare data for machine learning model using ChatGPT: Lesson 1

Lecture 175 Prepare data for machine learning model using ChatGPT: Lesson 2

Lecture 176 Create a machine learning model using the Linear Regression algorithm

Lecture 177 Develop machine learning model using ChatGPT

Lecture 178 Perform Feature Engineering using ChatGPT

Lecture 179 Performing Hyperparameter Optimization using ChatGPT

Section 23: Logistic Regression Algorithm with ChatGPT

Lecture 180 Loading Dataset using ChatGPT

Lecture 181 Perform initial analysis on Dataset using ChatGPT

Lecture 182 Performing the first operation on the Dataset using ChatGPT

Lecture 183 Tackling Missing values using ChatGPT: Lesson 1

Lecture 184 Tackling Missing values using ChatGPT: Lesson 2

Lecture 185 Tackling Missing values using ChatGPT: Lesson 3

Lecture 186 Tackling Missing values using ChatGPT: Lesson 4

Lecture 187 Performing Bivariate analysis with CatPLot using ChatGPT

Lecture 188 Performing Bivariate analysis with KdePLot using ChatGPT

Lecture 189 Examining the correlation of variables using ChatGPT: Lesson 1

Lecture 190 Examining the correlation of variables using ChatGPT: Lesson 2

Lecture 191 Perform a get_dummies operation using ChatGPT

Lecture 192 Prepare for Logistic Regression modeling using ChatGPT

Lecture 193 Create a Logistic Regression model using ChatGPT

Lecture 194 Examining evaluation metrics on the Logistic Regression model using ChatGPT - 1

Lecture 195 Examining evaluation metrics on the Logistic Regression model using ChatGPT -2

Lecture 196 Perform a GridSearchCV operation using ChatGPT

Lecture 197 Model reconstruction with best parameters using ChatGPT

Section 24: Extra

Lecture 198 Generative AI & ChatGPT Mastery for Data Science and Python

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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