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Generative AI & ChatGPT Mastery for Data Science and Python
Rating: 4.6 out of 5(24 ratings)
211 students

Generative AI & ChatGPT Mastery for Data Science and Python

Master Generative AI, ChatGPT and Prompt Engineering for Data Science and Python from scratch with hands-on projects
Created byOak Academy, OAK Academy Team, Ali̇ CAVDAR
Last updated 6/2026
English

What you'll 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 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

Course content

30 sections • 276 lectures • 33h 32m total length

  • What is Artificial Intelligence?7:17

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


    What is Generative AI and how is it used in Data Science?

    Generative AI refers to AI models that create content such as text, code, or images. In data science, it can generate synthetic datasets, automate reporting, and assist with predictive modeling.

  • Artificial Narrow Intelligence (ANI)5:34

    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.


    What is ChatGPT and how can it help Python developers?

    ChatGPT is an AI language model by OpenAI that can generate, explain, and debug Python code. It helps developers automate tasks, learn Python faster, and solve coding problems efficiently.

  • Artificial General Intelligence (AGI)3:38

    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.


    How does Generative AI improve productivity in Data Science?

    Generative AI accelerates tasks like data preprocessing, feature engineering, report generation, and code creation, allowing data scientists to focus on higher-level analysis and model interpretation.

  • Artificial Super Intelligence (ASI)3:38

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


    Can ChatGPT assist with machine learning model development?

    Yes, ChatGPT can provide code examples, explain algorithms, suggest model architectures, and help troubleshoot Python ML libraries like scikit-learn, TensorFlow, and PyTorch.

  • Subsets of Artificial Intelligence - Machine Learning3:40

    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.


    Is this course suitable for beginners in Python and AI?

    Yes, the course covers both foundational Python programming and Generative AI concepts, making it suitable for beginners as well as intermediate learners.

  • Subsets of Artificial Intelligence - Deep Learning3:43

    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.


    What Python libraries are covered in the course?

    The course covers key Python libraries for data science, including pandas, NumPy, matplotlib, scikit-learn, and frameworks for working with AI models like OpenAI’s API.

  • Machine Learning vs. Deep Learning3:35

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


    Can I use Generative AI for data visualization?

    Yes, Generative AI can help generate code for plots, charts, and dashboards, making data visualization faster and more accessible.

  • Machine Learning Study with a Real Example: Lesson 13:56

    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.


    What are common use cases of ChatGPT in data science workflows?

    Common use cases include automating code generation, debugging Python scripts, generating documentation, answering analytical questions, and assisting with ETL tasks.

  • Machine Learning Study with a Real Example: Lesson 24:33

    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.


    How does mastering ChatGPT improve my data science career?

    Mastering ChatGPT demonstrates advanced AI skills, accelerates project completion, improves coding efficiency, and opens opportunities for AI-focused roles in data science.

  • Large Language Models(LLM)5:07

    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.


    Is it safe to use ChatGPT for sensitive data projects?

    While ChatGPT is powerful, sensitive or confidential data should not be shared with external AI models. Always use anonymized datasets and follow security best practices.

  • Natural Language Processing(NLP)6:11

    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.


    What is Generative AI and how is it used in Data Science?

    Generative AI refers to AI models that create content such as text, code, or images. In data science, it can generate synthetic datasets, automate reporting, and assist with predictive modeling.

Requirements

  • A working computer (Windows, Mac, or Linux)
  • Motivation to learn the the second largest number of job postings relative AI among all others
  • Desire to learn 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

Description

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.


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 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!


Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

  • Seeing clearly

  • Hearing clearly

  • Moving through the course without distractions


You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for Download


Dive 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

Who this course is for:

  • 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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