English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 34 lectures (1h 59m) | 1.34 GB
Beginner and advanced chatGPT use cases – hypothesis testing, supervised learning, Naive Bayes, ethics in AI
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.
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
Table of Contents
Introduction to the data science process and chatGPT
1 Introduction
2 Traditional data science methods and the role of ChatGPT
3 How to install chatGPT
4 How ChatGPT can boost your productivity
Data science use cases
5 Data preprocessing with ChatGPT
6 First attempt at machine learning with ChatGPT
7 Analyzing a client database with ChatGPT in Python
8 Analyzing a client database with ChatGPT in Python – analyzing top products
9 Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM
10 Exploratory data analysis (EDA) with ChatGPT – histogram and scatter plot
11 Exploratory data analysis (EDA) with ChatGPT – correlation matrix, outlier detec
12 Assignment 1
13 Hypothesis testing with ChatGPT
14 Marvels comic book database Intro to Regular Expressions (RegEx)
15 Decoding comic book data Python Regular Expressions and ChatGPT
16 Assignment 3
17 Algorithm recommendation Movie Database Analysis with ChatGPT
18 Algorithm recommendation recommendation engine for movies with ChatGPT
19 Assignment 4
20 Ethical principles in data and AI utilization
21 Using ChatGPT for ethical considerations
Intro to the case study
22 Intro to the case study
23 Naïve Bayes
24 Tokenization and Vectorization
25 Imbalanced data sets
26 Overcome imbalanced data in machine learning
27 Model performance metrics
Case study of user reviews and sentiment analysis
28 Loading the Dataset and Preprocessing
29 Optimizing user reviews data preprocessing & EDA
30 Reg Ex for Analyzing Text Review Data
31 Understanding Differences between Multinomial and Bernouilli Naive Bayes
32 Machine learning with Naïve Bayes (first attempt)
33 Machine learning with Naïve Bayes – converting the problem to a binary one
34 Testing the model on new data
Resolve the captcha to access the links!