+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: Generative Ai & Chatgpt Mastery For Data Science And Python (/Thread-Generative-Ai-Chatgpt-Mastery-For-Data-Science-And-Python)
Generative Ai & Chatgpt Mastery For Data Science And Python - AD-TEAM - 12-22-2024
![[Image: 516b9e06665d0330849fe5034eea446f.jpg]](https://i124.fastpic.org/big/2024/1222/6f/516b9e06665d0330849fe5034eea446f.jpg)
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.
![[Image: LS4Jhi73_o.jpg]](https://images2.imgbox.com/56/b3/LS4Jhi73_o.jpg)