Generative AI & ChatGPT Mastery for Data Science and Python from Udemy

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 projects

Artificial 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.

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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 python

Python'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 Learning
- Subsets of Artificial Intelligence - Deep Learning
- Machine Learning vs. 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
- The Revolution of the Age: Creating a ChatGPT Account
- Let's Get to Know the ChatGPT Interface
- ChatGPT: Differences Between Versions
- Differences in the ChatGPT-4 Interface
- ChatGPT's Endpoints
- ChatGPT's Secret to More Accurate Answers: 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?
- As an App: ChatGPT
- Voice Communication with ChatGPT-4o
- Instant Translation in 50+ Languages
- Interview Preparation with ChatGPT-4o
- Visual Commentary with ChatGPT-4o
- Getting to know the dataset using ChatGPT
- Getting started with Exploratory Data Analysis(EDA) using ChatGPT
- Perform Univariate Analysis using ChatGPT
- Perform Bivariate Analysis using ChatGPT
- Perform Multivariate Analysis using ChatGPT
- Perform Correlation 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
- 2.1 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

Summary

Beginners 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.

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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

What's inside

Learning objectives

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?
Feature scaling with the robustscaler method for machine learning algorithms
Chronological development of chatgpt
What are the capabilities of chatgpt-4o?
Voice communication with chatgpt-4o
Logistic regression algorithm

Instant translation in 50+ languages
Interview preparation with chatgpt-4o
Cross validation
Visual commentary with chatgpt-4o
Data analysis is the process of studying or manipulating a dataset to gain some sort of insight
As an app: chatgpt
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
Roc curve and area under curve (auc)
Prepare data for machine learning model using chatgpt
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
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
Show more
Show less

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?
Feature scaling with the robustscaler method for machine learning algorithms
Chronological development of chatgpt
What are the capabilities of chatgpt-4o?
Voice communication with chatgpt-4o
Logistic regression algorithm
Instant translation in 50+ languages
Interview preparation with chatgpt-4o
Cross validation
Visual commentary with chatgpt-4o
Data analysis is the process of studying or manipulating a dataset to gain some sort of insight
As an app: chatgpt
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
Roc curve and area under curve (auc)
Prepare data for machine learning model using chatgpt
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
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
Show more
Show less

Syllabus

In this lesson, we will conduct a hands-on Machine Learning study using a real-world example, starting from data preparation to model selection and evaluation.

In this lesson, we will explore the fundamental concepts and definitions of Artificial Intelligence, understanding its scope and significance in modern technology.

In this lesson, we will dive into Artificial Narrow Intelligence (ANI), also known as Weak AI, and discuss its capabilities and limitations with examples from current AI applications.

In this lesson, we will explore the concept of Artificial General Intelligence (AGI), or Strong AI, focusing on its theoretical potential to perform any intellectual task that a human can do.

In this lesson, we will discuss Artificial Super Intelligence (ASI), its hypothetical scenarios, and the implications of AI surpassing human intelligence.

In this lesson, we will break down the subset of AI known as Machine Learning, examining how machines learn from data and the different approaches within this field.

In this lesson, we will delve into Deep Learning, a more advanced subset of Machine Learning, exploring how neural networks and layered architectures work to mimic human brain functions.

In this lesson, we will compare and contrast Machine Learning with Deep Learning, highlighting their key differences, strengths, and appropriate use cases.

In this lesson, we will continue the hands-on Machine Learning study, refining the model, tuning hyperparameters, and analyzing the results to draw meaningful conclusions.

In this lesson, we will explore Large Language Models (LLMs), discussing how they are trained, their applications, and the impact they have had on natural language processing and AI development.

In this lesson, we will cover the fundamentals of Natural Language Processing (NLP), including key techniques, algorithms, and the challenges involved in teaching machines to understand and generate human language.

In this lesson, we will discuss the considerations and potential challenges users may face before switching to ChatGPT, including data privacy and AI limitations.

In this lesson, we will explore OpenAI, the organization behind ChatGPT, and its revolutionary role in advancing artificial intelligence.

In this lesson, we will guide you through the process of creating a ChatGPT account, ensuring you can access and start using the platform effectively.

In this lesson, we will take a tour of the ChatGPT interface, familiarizing ourselves with the features and tools available to users.

In this lesson, we will compare the different versions of ChatGPT, highlighting the key differences and improvements made in each iteration.

In this lesson, we will examine the changes and enhancements specific to the ChatGPT-4 interface, focusing on user experience and functionality.

In this lesson, we will dive into ChatGPT's endpoints, explaining how to effectively use them for various tasks, including generating text, answering questions, and more.

In this lesson, we will uncover how crafting effective prompts can significantly enhance the accuracy and relevance of ChatGPT's responses.

In this lesson, we will delve into the first set of techniques and strategies for prompt engineering, focusing on maximizing the efficiency of your interactions with ChatGPT.

In this lesson, we will explore advanced techniques in prompt engineering, building on the foundations laid in Lesson 1 to create even more powerful prompts.

In this lesson, we will continue our deep dive into prompt engineering, introducing complex scenarios and multi-layered prompts to extract detailed responses.

In this lesson, we will further refine our prompt engineering skills, focusing on precision and clarity to ensure the best possible outputs from ChatGPT.

In this lesson, we will summarize the key principles and strategies of prompt engineering, providing a comprehensive overview for easy reference.

In this lesson, we will review a variety of sample prompts, demonstrating the practical application of prompt engineering techniques in different contexts.

In this lesson, we will explore the most effective questions to ask in prompt engineering, starting with foundational techniques to guide AI responses.

In this lesson, we will continue our exploration of best questions in prompt engineering, focusing on how to frame queries to elicit precise and relevant answers.

In this lesson, we will delve deeper into crafting strategic questions in prompt engineering, enhancing the depth and detail of AI-generated content.

In this lesson, we will further refine our question-asking techniques, emphasizing clarity and context to improve the quality of ChatGPT's outputs.

In this lesson, we will conclude our exploration of best questions with advanced strategies for complex prompt engineering scenarios.

In this lesson, we will summarize the key insights and strategies for asking the best questions in prompt engineering, providing a comprehensive overview.

In this lesson, we will reinforce the concepts learned by applying them in a practical scenario, demonstrating the impact of well-crafted questions on AI interactions.

In this lesson, we will outline the steps to effectively construct a roadmap for creating precise and effective prompts, ensuring clarity and direction in AI interactions.

In this lesson, we will focus on the technique of Directed Writing Requests, exploring how to guide ChatGPT to produce content that meets specific needs and criteria.

In this lesson, we will delve into the Clear Explanation Method, a strategy to enhance the clarity and comprehensiveness of AI-generated responses.

In this lesson, we will explore Example-Based Learning, showing how providing examples can significantly improve the quality and relevance of AI outputs.

In this lesson, we will introduce the RGC framework (Role, Goals, Context), a structured approach to crafting prompts that align with specific roles, objectives, and contextual needs.

In this lesson, we will explore techniques for constraining responses in ChatGPT, ensuring that the output stays within desired boundaries and relevance.

In this lesson, we will examine how to enhance the visual appeal of responses, focusing on formatting, structure, and presentation for better readability and engagement.

In this lesson, we will cover the first set of updates to prompt engineering, incorporating new methods and techniques for more effective AI interactions.

In this lesson, we will continue exploring prompt updates, focusing on refining strategies to improve clarity and precision in AI responses.

In this lesson, we will delve into advanced updates in prompt engineering, introducing more complex scenarios and multi-layered prompts.

In this lesson, we will finalize our exploration of prompt updates, summarizing key improvements and best practices for optimal AI interactions.

In this lesson, we will explore the ChatGPT-Google extension, learning how to integrate and use it to enhance your browsing experience.

In this lesson, we will focus on the art of email writing using ChatGPT, covering tips and techniques to craft effective and professional emails.

In this lesson, we will delve into summarizing YouTube videos with ChatGPT, learning how to extract key points and create concise summaries.

In this lesson, we will discuss how to effectively communicate with ChatGPT, maximizing its capabilities for productive and meaningful conversations.

In this lesson, we will learn how to quickly access ChatGPT, utilizing shortcuts and methods to streamline your workflow.

In this lesson, we will explore how to dive into websites using ChatGPT, extracting valuable information and insights efficiently.

In this lesson, we will focus on getting prompt assistance from ChatGPT, ensuring that you can quickly and accurately receive the help you need for various tasks.

In this lesson, we will explore how to use the ChatGPT API, enabling integration with other applications and automating tasks using AI.

In this lesson, we will focus on file reading with ChatGPT, learning how to extract and analyze information from various types of files.

In this lesson, we will delve into visual reading, teaching ChatGPT to interpret and summarize visual content effectively.

In this lesson, we will introduce DALL-E for visual generation, covering the basics of creating images from text prompts.

In this lesson, we will enhance images using DALL-E, focusing on refining visual outputs to match specific requirements.

In this lesson, we will explore how to improve visuals through the use of ready-made prompts, streamlining the process of generating high-quality images.

In this lesson, we will learn techniques for combining images, creating composite visuals that convey more complex ideas.

In this lesson, we will introduce a helper site for visual prompts, a resource that provides inspiration and examples for effective visual creation.

In this lesson, we will discuss GPTs, exploring their functionalities and how they can be tailored to specific tasks.

In this lesson, we will guide you through the process of creating your own GPT, customizing its behavior and output to meet your needs.

In this lesson, we will cover useful GPTs, focusing on the first set of applications that can enhance productivity and creativity.

In this lesson, we will continue exploring useful GPTs, introducing more advanced applications and their practical uses.

In this lesson, we will finalize our exploration of useful GPTs, summarizing key tools and techniques for maximizing their potential.

In this lesson, we will introduce ChatGPT-4o, highlighting the latest features and improvements in this version.

In this lesson, we will guide you on how to effectively use ChatGPT-4o, covering key functionalities and best practices.

In this lesson, we will explore the chronological development of ChatGPT, tracing its evolution from earlier versions to the current ChatGPT-4o.

In this lesson, we will examine the capabilities of ChatGPT-4o, showcasing its strengths and unique features that set it apart from previous iterations.

In this lesson, we will discuss the ChatGPT app, exploring how it functions as a mobile application and its advantages for on-the-go use.

In this lesson, we will focus on voice communication with ChatGPT-4o, demonstrating how to interact with the AI using spoken language.

In this lesson, we will explore the instant translation feature in ChatGPT-4o, which supports over 50 languages, making it a powerful tool for global communication.

In this lesson, we will guide you through interview preparation using ChatGPT-4o, focusing on how the AI can assist in mock interviews and skill refinement.

In this lesson, we will begin a series on visual commentary using ChatGPT-4o, exploring how to create and analyze visual content effectively.

In this lesson, we will continue our exploration of visual commentary with ChatGPT-4o, delving deeper into techniques for enhancing visual presentations.

In this lesson, we will provide the sources used throughout the project for further reference and study.

This lecture is a resource lecture. You will find all the prompts we asked ChatGPT throughout the course.

In this lesson, we will provide a link to the project's Github repository, containing all the relevant code.

In this lesson, we will provide a link to the Kaggle page where the dataset and notebook can be accessed.

In this lesson, we will introduce ChatGPT for Generative AI and how it can assist in exploring datasets.

In this lesson, we will guide you through the process of accessing the dataset for analysis.

In this lesson, we will focus on the first step of field knowledge and its importance in data exploration.

In this lesson, we will continue our exploration into field knowledge, enhancing our understanding of the dataset.

In this lesson, we will load the dataset and discuss how to understand and work with its variables.

In this lesson, we will delve deeper into the details of the dataset's variables and their significance.

In this lesson, we will perform the first analysis on the dataset to gain initial insights.

In this lesson, we will update the variable names to make them more meaningful and easier to interpret.

In this lesson, we will examine the dataset for missing values and discuss ways to handle them.

In this lesson, we will focus on examining unique values within the dataset.

In this lesson, we will begin examining the statistics of the dataset's variables, focusing on key metrics.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Integrates ChatGPT-4o, which can automate tasks, generate code, answer queries, and offer suggestions for better analysis and model optimization

Covers exploratory data analysis, which is a key step before jumping into predictive modeling and machine learning algorithms

Explores large language models and natural language processing, which are essential for understanding and generating human language

Teaches prompt engineering, which can significantly enhance the accuracy and relevance of responses from generative AI models

Includes a section on GPTs, exploring their functionalities and how they can be tailored to specific tasks and applications

Requires learners to create an account with OpenAI, which may pose a barrier to entry for some students

Reviews summary

Generative ai & chatgpt for data science

According to learners, this course offers a solid introduction to the integration of Generative AI, specifically ChatGPT, into the Data Science and Python workflow. Students highlight the hands-on approach and practical examples, finding them particularly useful for applying concepts immediately. The coverage of Prompt Engineering is seen as a key strength, providing actionable techniques to leverage AI tools effectively. While generally well-received, some feedback suggests that the course could potentially delve deeper into advanced Machine Learning algorithms or assume less prior familiarity with foundational Python or ML concepts for some learners.

Instructors explain concepts clearly.

"The instructor explains complex topics in an easy-to-understand manner."

"Lectures are well-structured and easy to follow."

"I found the explanations clear and concise, making it easy to keep up."

"The teaching style is effective for understanding the material."

Covers AI, ML, Python, and ChatGPT.

"The course covers a wide range of relevant topics from AI basics to specific ML algorithms."

"It provides a good overview of both traditional data science and the new AI tools."

"I liked that it brought together Python, ML, and Generative AI concepts."

"Provides a solid foundation across multiple important areas in the field."

Techniques for effective AI interaction.

"The section on Prompt Engineering provided clear and useful techniques."

"Learning how to write better prompts significantly improved my results with ChatGPT."

"I found the specific examples of prompts for data tasks very helpful."

"Understanding prompt engineering is key to getting the most out of AI tools like ChatGPT."

Focus on practical examples and projects.

"The hands-on exercises are extremely valuable and help solidify understanding."

"I really enjoyed the practical examples throughout the course; they made the concepts clear."

"The project-based approach is the best way to learn these topics."

"Applying what I learned immediately through coding examples was very effective."

Shows how to use ChatGPT in data tasks.

"I learned how to effectively integrate ChatGPT into my data science projects."

"The practical demonstrations of using ChatGPT for EDA and modeling were insightful."

"Using ChatGPT to help with data cleaning and code generation was a game changer."

"I appreciated seeing how ChatGPT can be a powerful assistant for data analysis tasks."

Desire for more advanced or detailed content.

"I wished some of the machine learning topics were covered in more depth."

"While the intro is good, it doesn't delve deeply into advanced ML concepts."

"Could benefit from exploring more complex datasets or scenarios."

"Some parts felt a bit rushed, could spend more time on specific algorithms."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI & ChatGPT Mastery for Data Science and Python with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python syntax and data structures. This will provide a solid foundation for using Python in data science and generative AI.

Browse courses on Python Basics

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Review basic data types (integers, floats, strings, booleans).
Practice writing simple functions and control flow statements (if/else, loops).
Familiarize yourself with common Python libraries like NumPy and Pandas.

Review 'Python Data Science Handbook'

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Enhance your understanding of Python data science libraries and their applications. This book will serve as a valuable reference for your data science projects.

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Read the chapters related to NumPy, Pandas, and Matplotlib.
Work through the code examples and try to apply them to your own datasets.
Use the book as a reference when working on data science projects.

Review 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of machine learning algorithms and their implementation. This book will help you solidify your knowledge of the concepts covered in the course.

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Read the chapters related to the machine learning algorithms covered in the course.
Work through the code examples and try to modify them to understand the concepts better.
Attempt the exercises at the end of each chapter to test your understanding.

Four other activities

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Practice Data Analysis with Pandas

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Improve your data analysis skills by working through practice exercises with Pandas. This will help you become more proficient in data manipulation and analysis.

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Find a dataset online (e.g., from Kaggle) to practice with.
Use Pandas to load, clean, and explore the dataset.
Perform various data analysis tasks, such as filtering, grouping, and aggregating data.

Build a Simple Chatbot with ChatGPT API

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Apply your knowledge of ChatGPT API to create a functional chatbot. This project will help you understand how to integrate generative AI into a real-world application.

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Set up a ChatGPT API account and obtain your API key.
Design the chatbot's functionality and user interface.
Write Python code to interact with the ChatGPT API and handle user input.
Test and refine your chatbot to improve its performance and user experience.

Write a Blog Post on Prompt Engineering Techniques

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Deepen your understanding of prompt engineering by explaining different techniques in a blog post. This will help you articulate your knowledge and share it with others.

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Research different prompt engineering techniques and their applications.
Write a clear and concise blog post explaining the techniques with examples.
Publish your blog post on a platform like Medium or your personal website.

Create a Data Visualization Dashboard

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Develop your data visualization skills by creating an interactive dashboard. This will help you communicate data insights effectively.

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Choose a dataset and identify key insights to visualize.
Select a data visualization tool (e.g., Tableau, Power BI, or Python with Plotly/Dash).
Create interactive charts and graphs to present the data insights.
Design a user-friendly dashboard layout and deploy it online.

Career center

Learners who complete Generative AI & ChatGPT Mastery for Data Science and Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses statistical methods, machine learning, and data visualization tools to analyze large datasets and extract insights. This course helps a data scientist build a foundation in exploratory data analysis, machine learning algorithms like Logistic Regression and Random Forest, and integrating AI tools like ChatGPT-4o to automate tasks and generate code. A data scientist who completes this course will gain a practical understanding of how to move from raw data to actionable insights. The course's emphasis on hands-on projects ensures that a data scientist can immediately apply learned skills to real-world data challenges.

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Machine Learning Engineer

A machine learning engineer develops, deploys, and maintains machine learning models in production environments. This course helps a machine learning engineer master machine learning algorithms and techniques, and also explore how to leverage the capabilities of ChatGPT-4o for code generation, model optimization, and workflow automation. A machine learning engineer may find the skills to implement and fine-tune algorithms, along with methods to evaluate model performance, extremely valuable. The course provides a strong foundation for a machine learning engineer to build and apply machine learning solutions effectively.

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AI Specialist

An artificial intelligence specialist works on the cutting edge of AI technologies, often focusing on research and development of new AI applications. This course may be especially useful for an AI specialist due to its comprehensive coverage of artificial intelligence subsets like machine learning and deep learning, as well as the integration of large language models and prompt engineering techniques. An AI specialist may also benefit from the course content that covers the practical application of AI tools such as ChatGPT-4o for visual and text processing, as well as the design and implementation of machine learning models. This course helps an AI specialist build a foundation of both theoretical and practical knowledge.

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Business Intelligence Analyst

A Business Intelligence analyst focuses on analyzing business data to identify trends and provide actionable insights that drive strategic decisions. This course helps a business intelligence analyst learn how to perform exploratory data analysis, visualize data, and prepare it for modeling. The course's focus on both machine learning techniques and how to work with ChatGPT-4o can enable a business intelligence analyst to automate tasks, generate code, and explore data to make data-driven decisions. This course's exploration of data analysis techniques is particularly helpful for a business intelligence analyst.

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Data Analyst

A data analyst examines datasets to derive meaningful conclusions and inform decision making. The course may help a data analyst learn to perform exploratory data analysis, visualize data, and prepare it for modeling. The course's integration of ChatGPT-4o could be useful for a data analyst to automate tasks, generate code, and find better ways to analyze and present data. Practical experience gained from the course may assist a data analyst to handle real world data challenges and improve their workflow.

See salaries and explore the career path for Data Analyst

AI Consultant

An AI consultant advises organizations on implementing artificial intelligence solutions to improve business processes. This course may be useful for an AI consultant, as it covers key aspects of artificial intelligence, machine learning, and natural language processing with a focus on real-world applications. An AI consultant may also benefit from learning to use a tool like ChatGPT-4o for code generation, workflow automation, or assisting clients with data analysis tasks. The course provides a strong foundation for an AI consultant to develop effective and viable AI strategies.

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Research Scientist

A research scientist explores complex problems and develops innovative solutions using data-driven approaches. This course may be very useful for a research scientist because it provides a strong background in data science, machine learning, and AI. A research scientist may benefit from the course's coverage of machine learning algorithms, data visualization techniques, and AI tools, as this can enhance their ability to conduct research and analyze data. Additionally, the course's emphasis on real-world applications can help a research scientist to make practical decisions based on results in the real world. A research scientist would need an advanced degree, such as a masters or phd.

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Quantitative Analyst

A quantitative analyst, often working in finance, models data to make predictions and inform trading decisions. This course may be helpful for a quantitative analyst by providing hands-on experience with exploratory data analysis, machine learning algorithms, and how to leverage ChatGPT-4o to improve workflows. A quantitative analyst may find that their ability to build machine learning models and use the latest AI tools enhances their analytical capabilities. The course may also help a quantitative analyst through its coverage of practical applications and hyperparameter tuning, leading to more accurate and efficient predictive models.

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Software Developer

A software developer designs, writes, and tests code for applications and systems. This course may be useful for a software developer because it provides practical experience using Python for data analysis and machine learning, which can be integrated into software applications. Using AI tools like ChatGPT-4o, the software developer may find opportunities to enhance code generation and improve development efficiency. The course's focus on practical projects can help a software developer to apply these techniques in real-world scenarios, making them more effective at their jobs.

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Computational Linguist

A computational linguist develops computational models of human language. A course covering the use of large language models and natural language processing techniques may be useful for a computational linguist as it explores topics such as prompt engineering and integrating AI tools. A computational linguist may also find the course's focus on the practical implications of the technology, such as for visual and textual processing, to be particularly helpful. The course provides a unique opportunity to learn how state-of-the-art AI tools are applied in real-world tasks.

See salaries and explore the career path for Computational Linguist

Statistician

A statistician collects, analyzes, and interprets numerical data to derive insights and make informed decisions. This course may be helpful for a statistician as it provides a foundation for conducting exploratory data analysis and using machine learning algorithms. A statistician may find this course helpful for learning to use a tool like ChatGPT-4o to perform data analysis and model generation. The course has practical exploration of data analysis techniques that may also be valuable.

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Research Analyst

A research analyst gathers and interprets data to inform research studies and reports. This course may be helpful for a research analyst to learn how to conduct exploratory data analysis and how to build and evaluate machine learning models. The course's focus on leveraging ChatGPT-4o can increase the efficiency and speed of research analysis through automated code generation and workflow enhancements. The course helps a research analyst learn to apply data science techniques to real-world research scenarios.

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Financial Analyst

A financial analyst examines financial data and provides recommendations to improve investment strategies. This course may be useful for a financial analyst because it provides a foundation in data analysis and machine learning alongside the uses of large language models like ChatGPT. A financial analyst may find this course helpful in order to better leverage data and improve methods of model creation. The course might help a financial analyst to better perform their job by offering a unique perspective driven by advanced AI.

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Marketing Analyst

A marketing analyst interprets marketing data to develop effective marketing strategies. This course may be useful for a marketing analyst to gain a better understanding of data analysis and machine learning techniques. A marketing analyst may benefit from learning how to use ChatGPT-4o to automate tasks, explore data insights, and improve data-driven decision-making. The course provides a good mix of theoretical and practical experience that may be helpful to a marketing analyst.

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Technical Writer

A technical writer creates documentation for technical products or services. This course may be useful for a technical writer to learn the fundamental concepts of AI and tools such as ChatGPT. A technical writer might learn how to use prompt engineering, as well as how large language models and other technology work. A technical writer who takes this course may find themselves better prepared to explain complex systems in simple terms.

See salaries and explore the career path for Technical Writer

Generative AI & ChatGPT Mastery for Data Science and Python

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