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ChatGPT for Python Data Science and Machine Learning

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ChatGPT for Python Data Science and Machine Learning, available at $54.99, has an average rating of 4.61, with 191 lectures, 7 quizzes, based on 134 reviews, and has 1876 subscribers.

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You will learn about Use ChatGPT for real-life Data Science and Machine Learning Projects Let ChatGPT write do the Coding work (Python, Pandas, scikit-learn etc.) Use ChatGPT to select the most suitable Machine Learning Model Use ChatGPT to analyse and interpret the outcomes of Machine Learning & Statistical Models Perform an Explanatory Data Analysis with ChatGPT and Python Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas Coding & more Use ChatGPT to fit and evaluate Regression and Classification Models Use ChatGPT for Multiple Regression Analysis and Hypothesis Testing Use ChatGPT for Error Handling and Troubleshooting Master Clustering and Unsupervised Learning with ChatGPT This course is ideal for individuals who are Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch. or Data Scientists interested in boosting their work with Artificial Intelligence. or Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work. or Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT. or Machine Learning Wizards needing help and assistance for their models from ChatGPT. It is particularly useful for Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch. or Data Scientists interested in boosting their work with Artificial Intelligence. or Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work. or Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT. or Machine Learning Wizards needing help and assistance for their models from ChatGPT.

Enroll now: ChatGPT for Python Data Science and Machine Learning

Summary

Title: ChatGPT for Python Data Science and Machine Learning

Price: $54.99

Average Rating: 4.61

Number of Lectures: 191

Number of Quizzes: 7

Number of Published Lectures: 191

Number of Published Quizzes: 7

Number of Curriculum Items: 198

Number of Published Curriculum Objects: 198

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Use ChatGPT for real-life Data Science and Machine Learning Projects
  • Let ChatGPT write do the Coding work (Python, Pandas, scikit-learn etc.)
  • Use ChatGPT to select the most suitable Machine Learning Model
  • Use ChatGPT to analyse and interpret the outcomes of Machine Learning & Statistical Models
  • Perform an Explanatory Data Analysis with ChatGPT and Python
  • Use ChatGPT for Data Manipulation, Aggregation, advanced Pandas Coding & more
  • Use ChatGPT to fit and evaluate Regression and Classification Models
  • Use ChatGPT for Multiple Regression Analysis and Hypothesis Testing
  • Use ChatGPT for Error Handling and Troubleshooting
  • Master Clustering and Unsupervised Learning with ChatGPT

Who Should Attend

  • Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch.
  • Data Scientists interested in boosting their work with Artificial Intelligence.
  • Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work.
  • Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT.
  • Machine Learning Wizards needing help and assistance for their models from ChatGPT.

Target Audiences

  • Beginners seeking to master real-life Data Science Projects in no time without the need to learn everything from scratch.
  • Data Scientists interested in boosting their work with Artificial Intelligence.
  • Everybody in a Data-related Profession wanting to leverage the power of ChatGPT for their day-to-day work.
  • Data Analysts seeking to outsource the most time-consuming parts of their work to ChatGPT.
  • Machine Learning Wizards needing help and assistance for their models from ChatGPT.

### Updated: Now including the latest models  GPT-4o and GPT-4o mini ###

 

Welcome to the first Data Science and Machine Learning course with ChatGPT. Learn how to use ChatGPT to master complex Data Science and Machine Learning real-life projects in no time!

Why is this a game-changing course?

Real-world Data Science and Machine Learning projects require a solid background in advanced statistics and Data Analytics. And it would be best if you were a proficient Python Coder. Do you want to learn how to master complex Data Science projects without the need to study and master all the required basics (which takes dozens if not hundreds of hours)? Then this is the perfect course for you! 

What you can do at the end of the course:

At the end of this course, you will know and understand all strategies and techniques to master complex Data Science and Machine Learning projects with the help of ChatGPT! And you don´t have to be a Data Science or Python Coding expert! Use ChatGPT as your assistant and let ChatGPT do the hard work for you!Use ChatGPT for

  1. the theoretical part

  2. Python coding

  3. evaluating and interpreting coding and ML results

This course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts to

  • load, initially inspect, and understand unknown datasets

  • clean and process raw datasets with Pandas

  • manipulate, aggregate, and visualize datasets with Pandas and matplotlib

  • perform an extensive Explanatory Data Analysis (EDA) for complex datasets

  • use advanced statistics, multiple regression analysis, and hypothesis testing to gain further insights

  • select the most suitable Machine Learning Model for your prediction tasks (Model Selection)

  • evaluate and interpret the performance of your Machine Learning models (Performance Evaluation)

  • optimize your models via handling Class Imbalance, Hyperparameter Tuning & more.

  • evaluate and interpret the results and findings of your predictions to solve real-world business problems

  • master regression, classification, and unsupervised learning/clustering projects

We´ll cover prompting strategies and tactics for GPT-3.5 / GPT-4o mini (free) and GPT-4 / GPT-4o (paid subscription). Know the differences and master both!

The course is organized into Do-it-yourself projects with detailed project assignments and supporting materials. At the end, you will find a video sample solution. All solutions and sample prompts are available for simple download or copy/paste!

Who is this Course for?

  • Data Science Beginners who have no time to learn everything from scratch

  • Skilled Data Scientists seeking to outsource the most time-consuming parts of their work to save time   

Are you ready to be at the forefront of AI in Data Science? Enroll now and start transforming your professional landscape with AI and ChatGPT!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Welcome and Introduction

Lecture 2: Sneak Preview: Data Science with ChatGPT

Lecture 3: How to get the most out of this course

Lecture 4: Course Overview

Lecture 5: Course Materials /Downloads

Chapter 2: Introduction to ChatGPT

Lecture 1: What is ChatGPT and how does it work?

Lecture 2: ChatGPT vs. Search Engines

Lecture 3: Artificial Intelligence vs. Human Intelligence

Lecture 4: Creating a ChatGPT account and getting started

Lecture 5: **Update July 2024**

Lecture 6: Features, Options and Products around GPT models

Lecture 7: Update (July 2024): Products and Availability (FREE vs. PLUS)

Lecture 8: Navigating the OpenAI Website

Lecture 9: What is a Token and how do Tokens work?

Lecture 10: Prompt Engineering Techniques (Part 1)

Lecture 11: Prompt(s) used in previous Lecture

Lecture 12: Prompt Engineering Techniques (Part 2)

Lecture 13: Prompt(s) used in previous Lecture

Lecture 14: Prompt Engineering Techniques (Part 3)

Lecture 15: Prompt(s) used in previous Lecture

Chapter 3: Installing and working with Python, Anaconda and Jupyter Notebooks

Lecture 1: Download and Install Anaconda

Lecture 2: How to open Jupyter Notebooks

Lecture 3: How to work with Jupyter Notebooks

Chapter 4: Introduction Project: Explore an unknown Dataset with ChatGPT and Pandas

Lecture 1: Project Introduction

Lecture 2: GPT Model Upgrades (July 24)

Lecture 3: Project Assignment

Lecture 4: Providing the Dataset to GPT-3.5 / GPT-4o mini

Lecture 5: Prompt(s) used in previous Lecture

Lecture 6: Inspecting the Dataset with GPT-3.5 / GPT-4o mini

Lecture 7: Prompt(s) used in previous Lecture

Lecture 8: Brainstorming with GPT-3.5 / GPT-4o mini

Lecture 9: Prompt(s) used in previous Lecture

Lecture 10: Data Cleaning with GPT-3.5 / GPT-4o mini

Lecture 11: Prompt(s) used in previous Lecture

Lecture 12: Data Transformation and Feature Engineering with GPT-3.5 / GPT-4o mini

Lecture 13: Prompt(s) used in previous Lecture

Lecture 14: Loading the Dataset with GPT4 / GPT-4o

Lecture 15: Prompt(s) used in previous Lecture

Lecture 16: Initial Data Inspection and Brainstorming with GPT4 / GPT-4o

Lecture 17: Prompt(s) used in previous Lecture

Lecture 18: Data Cleaning with GPT4 / GPT-4o

Lecture 19: Prompt(s) used in previous Lecture

Lecture 20: Data Transformation and Feature Engineering with GPT4 / GPT-4o

Lecture 21: Prompt(s) used in previous Lecture

Lecture 22: How to download and save the cleaned Dataset from GPT4 / GPT-4o

Lecture 23: Prompt(s) used in previous Lecture

Lecture 24: Conclusion, Final Remarks and Troubleshooting

Chapter 5: Using ChatGPT for complex Data Wrangling and Manipulation Tasks

Lecture 1: Project Introduction

Lecture 2: Project Assignment

Lecture 3: Task 1 – Loading and Sorting

Lecture 4: Prompt(s) used in the previous Lecture

Lecture 5: Task 2 – Data Type Conversion

Lecture 6: Prompt(s) used in the previous Lecture

Lecture 7: Task 3 – Mapping

Lecture 8: Prompt(s) used in the previous Lecture

Lecture 9: Task 4 – Reversing One-Hot-Encoding

Lecture 10: Prompt(s) used in the previous Lecture

Lecture 11: Excursus: Saving Intermediate Results

Lecture 12: Task 5: Selecting Columns and their sequence

Lecture 13: Prompt(s) used in the previous Lecture

Lecture 14: Task 6: Unique and most frequent values

Lecture 15: Prompt(s) used in the previous Lecture

Lecture 16: Task 7: Grouping and Aggregating DataFrames

Lecture 17: Prompt(s) used in the previous Lecture

Lecture 18: Task 8: Advanced Filtering

Lecture 19: Prompt(s) used in the previous Lecture

Lecture 20: Task 9: Adding group-specific Features

Lecture 21: Prompt(s) used in the previous Lecture

Lecture 22: Task 10: Identifying and fixing erroneous or non-intuitive Data

Lecture 23: Prompt(s) used in the previous Lecture

Lecture 24: Task 11: Index Operations

Lecture 25: Prompt(s) used in the previous Lecture

Lecture 26: Excursus: Understanding and Handling Warnings

Lecture 27: Data Wrangling and Manipulation with GPT-4 / GPT-4o

Lecture 28: Prompt(s) used in the previous Lecture

Chapter 6: Using ChatGPT for Explanatory Data Analysis (EDA)

Lecture 1: Project Introduction

Lecture 2: Project Assignment

Lecture 3: Task 1: (Up-) Loading the Dataset and first Inspection

Lecture 4: Prompt(s) used in the previous Lecture

Lecture 5: Task 2: Brainstorming: Goals and Objectives of an EDA

Lecture 6: Prompt(s) used in the previous Lecture

Lecture 7: Task 3: Feature Engineering and Creation

Lecture 8: Prompt(s) used in the previous Lecture

Lecture 9: Task 4: Univariate Data Analysis

Lecture 10: Prompt(s) used in the previous Lecture

Lecture 11: Excursus: Troubleshooting

Lecture 12: Task 5: Multivariate Data Analysis: Correlations

Lecture 13: Prompt(s) used in the previous Lecture

Lecture 14: Task 6: Exploring Factors influencing Appointment No-Shows (Part 1)

Lecture 15: Prompt(s) used in the previous Lecture

Lecture 16: Task 6: Exploring Factors Influencing Appointment No-Shows (Part 2)

Lecture 17: Task 7: Exploring Factors influencing SMS reminders

Lecture 18: Prompt(s) used in the previous Lecture

Lecture 19: The Code reviewed

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 1 votes
  • 3 stars: 11 votes
  • 4 stars: 39 votes
  • 5 stars: 80 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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