Chatgpt For Data Science And Machine Learning

Beginner and advanced chatGPT use cases - hypothesis testing, supervised learning, Naive Bayes, ethics in AI

What you'll learn

Perform exploratory data analysis (EDA)

Use RegEx

Hypothesis testing

Naive Bayes for sentiment analysis

Movie recommendation engine

Ethics and biases in data and the role of AI

Requirements

Some python and data science knowledge

access to ChatGPT plus

Description

Welcome to the ultimate ChatGPT and Python Data Science course—your golden ticket to mastering the art of data science intertwined with the latest AI technology from OpenAI.This course isn't just a learning journey—it's a transformative experience designed to elevate your skills and empower you with practical knowledge.With AI's recent evolution, many tasks can be accelerated using models like ChatGPT. We want to share how to leverage AI it for data science tasks.Embark on a journey that transcends traditional learning paths. Our curriculum is designed to challenge and inspire you through:Comprehensive Challenges: Tackle 10 concrete data science challenges, culminating in a case study that leverages our unique 365 data to address genuine machine learning problems.Real-World Applications: From preprocessing with ChatGPT to dissecting a furniture retailer's client database, explore a variety of industries and data types.Advanced Topics: Delve into retail data analysis, utilize regular expressions for comic book analysis, and develop a ChatGPT-powered movie recommendation system. Engage with such critical topics as AI ethics to combat biases and ensure data privacy.This course emphasizes practical application over theoretical knowledge, where you will:Perform dynamic sentiment analysis using a Naïve Bayes algorithm.Craft nuanced classification reports with our proprietary data.Gain hands-on experience with real datasets—preparing you to solve complex data science problems confidently.We’ll be using ChatGPT, Python, and Jupyter Notebook throughout the course, and I’ll link all the datasets, Notebooks for you to play around with on your own.I'll help you create a ChatGPT profile, but I’ll assume you're adept in Python and somewhat experienced in machine learning. Are you ready to dive into the future of data science with ChatGPT and Python?Join us now to unlock the full potential of AI and turn knowledge into action.Let's embark on this exciting journey together!

Overview

Section 1: Introduction to the data science process and chatGPT

Lecture 1 Introduction

Lecture 2 Traditional data science methods and the role of ChatGPT

Lecture 3 How to install chatGPT

Lecture 4 How ChatGPT can boost your productivity

Section 2: Data science use cases

Lecture 5 Data preprocessing with ChatGPT

Lecture 6 First attempt at machine learning with ChatGPT

Lecture 7 Analyzing a client database with ChatGPT in Python

Lecture 8 Analyzing a client database with ChatGPT in Python – analyzing top products

Lecture 9 Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM

Lecture 10 Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot

Lecture 11 Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec

Lecture 12 Assignment 1

Lecture 13 Hypothesis testing with ChatGPT

Lecture 14 Marvels comic book database: Intro to Regular Expressions (RegEx)

Lecture 15 Decoding comic book data: Python Regular Expressions and ChatGPT

Lecture 16 Assignment 3

Lecture 17 Algorithm recommendation: Movie Database Analysis with ChatGPT

Lecture 18 Algorithm recommendation: recommendation engine for movies with ChatGPT

Lecture 19 Assignment 4

Lecture 20 Ethical principles in data and AI utilization

Lecture 21 Using ChatGPT for ethical considerations

Section 3: Intro to the case study

Lecture 22 Intro to the case study

Lecture 23 Naïve Bayes

Lecture 24 Tokenization and Vectorization

Lecture 25 Imbalanced data sets

Lecture 26 Overcome imbalanced data in machine learning

Lecture 27 Model performance metrics

Section 4: Case study of user reviews and sentiment analysis

Lecture 28 Loading the Dataset and Preprocessing

Lecture 29 Optimizing user reviews: data preprocessing & EDA

Lecture 30 Reg Ex for Analyzing Text Review Data

Lecture 31 Understanding Differences between Multinomial and Bernouilli Naive Bayes

Lecture 32 Machine learning with Naïve Bayes (first attempt)

Lecture 33 Machine learning with Naïve Bayes – converting the problem to a binary one

Lecture 34 Testing the model on new data

Data science professionals looking to advance their skillset,Data analysts looking to transition into data science,Anyone looking to leverage the power of AI and data science