ChatGPT for Data Science and Machine Learning

Name: ChatGPT for Data Science and Machine Learning
Rating: 4.5 (1452 reviews)

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

Created by365 Careers

Last updated 2/2024

English

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

Course content

4 sections • 34 lectures • 1h 59m total length

Introduction1:52
Traditional data science methods and the role of ChatGPT5:02
Explore traditional data science methods from problem framing to data cleaning, data collection, and analysis with machine learning insights, and examine how ChatGPT fits into the workflow.
How to install chatGPT1:43
How ChatGPT can boost your productivity1:57
Boost your data science productivity with ChatGPT's data analysis tool to upload data, converse with it, and drive exploratory data analysis, pre-processing, data analysis, visualizations, and machine learning interpretation.

Data preprocessing with ChatGPT4:38
First attempt at machine learning with ChatGPT4:21
Analyzing a client database with ChatGPT in Python3:32
Analyzing a client database with ChatGPT in Python – analyzing top products3:32
Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM3:32
Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot5:06
Perform exploratory data analysis on a real estate data set with ChatGPT, using histograms, scatter plots, and correlation analysis to reveal size, price, and location patterns, including outlier detection.
Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec5:05
Assignment 10:44
Hypothesis testing with ChatGPT3:54
Learn hypothesis testing with ChatGPT by performing a two-sample t-test on grades for remedial class attendance, using Levene's test for equal variances; p = 0.11, cannot reject the null.
Marvels comic book database: Intro to Regular Expressions (RegEx)1:49
Decoding comic book data: Python Regular Expressions and ChatGPT4:08
Assignment 30:46
Algorithm recommendation: Movie Database Analysis with ChatGPT2:48
Algorithm recommendation: recommendation engine for movies with ChatGPT4:01
Assignment 40:35
Ethical principles in data and AI utilization2:51
Using ChatGPT for ethical considerations5:01

Intro to the case study2:32
Naïve Bayes4:10
Tokenization and Vectorization5:25
Explore tokenization and vectorization for text classification with Naive Bayes, within the standard supervised learning pipeline of data preprocessing, feature selection, and model evaluation.
Imbalanced data sets2:18
Learn how imbalanced data in classification affects model performance, why accuracy can be misleading, and how to address imbalance using appropriate evaluation metrics.
Overcome imbalanced data in machine learning3:32
Model performance metrics5:31

Loading the Dataset and Preprocessing2:19
Optimizing user reviews: data preprocessing & EDA4:10
Reg Ex for Analyzing Text Review Data3:29
Understanding Differences between Multinomial and Bernouilli Naive Bayes3:30
Machine learning with Naïve Bayes (first attempt)5:51
Classify five-class reviews with a multinomial naive bayes pipeline using text preprocessing, tokenization, and vectorization, then assess with a classification report and confusion matrix, addressing imbalanced data via oversampling (smote).
Machine learning with Naïve Bayes – converting the problem to a binary one4:35
Testing the model on new data4:49

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!

Who this course is for:

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

ChatGPT for Data Science and Machine Learning

What you'll learn

Explore related topics

Course content

Introduction to the data science process and chatGPT4 lectures • 11min

Data science use cases17 lectures • 56min

Intro to the case study6 lectures • 23min

Case study of user reviews and sentiment analysis7 lectures • 29min

Requirements

Description

Who this course is for: