Use CUSTOM GPT to Learn Python Matplotlib (Just speak, no coding required) - AI Tutorial [28 mins]

#chatgpt #python #learning

In this 28-minute AI tutorial, we have something special in store for you. Get ready to discover the power of Python Matplotlib like never before, all with the help of a custom ChatGPT designed specifically for this purpose. No coding required!

Join me as we explore various plots and chart options while harnessing the capabilities of our custom ChatGPT. Whether you're new to Python or looking to level up your data visualization skills, this video is your gateway to simplified learning. Say goodbye to complex coding and dive straight into mastering Matplotlib with ease. Watch now and unlock the potential of data visualization with Python, guided by this custom ChatGPT!

[0:00-0:30] Introduction to using custom GPTs for creating Matplotlib visuals in Python.
[0:31-1:00] Demonstrating how to generate a line chart showing sales trends using voice commands.
[1:01-1:30] The GPT generates a visual chart and corresponding Python code based on the given prompt.
[1:31-2:00] Requesting a scatter plot of the top salespeople in each division with labeled data points.
[2:01-2:30] Analyzing the generated scatter plot and tweaking prompts for better visualization.
[2:31-3:00] Requesting a visualization of historical figures like Thomas Edison and Elvis Presley.
[3:01-3:30] Creating a histogram for average car sales prices across different sales divisions.
[3:31-4:00] Adjusting histogram settings and discussing the iterative process for desired outputs.
[4:01-4:30] Commanding the GPT to generate a bar plot and refining it for clarity and readability.
[4:31-5:00] Requesting a box plot to analyze differences in sales volumes between divisions.
[5:01-5:30] Iteratively refining the visualization and exploring alternative chart types for better representation.
[5:31-6:00] Adjusting the chart's color scheme and labels for improved legibility and aesthetics.
[6:01-6:30] Introduction to creating a stacked area chart for division sales throughout the year.
[6:31-7:00] Adjusting labels and refining the visualization of the stacked area chart.
[7:01-7:30] Requesting changes to the labels and background color for better clarity.
[7:31-8:00] Creating a paired bar chart for division sales comparison.
[8:01-8:30] Refining the bar chart to improve visualization and readability.
[8:31-9:00] Exploring a box plot to analyze differences in sales volumes between divisions.
[9:01-9:30] Iterative process of refining the box plot for better representation of data.
[9:31-10:00] Requesting a variety of data visualizations using map plot lib options.
[10:01-10:30] Analysis of different visualization outputs including heat maps and pie charts.
[10:31-11:00] Discussing the utility of custom GPTs in generating insightful data visualizations.
[11:00-15:00] Refinement of charts with aesthetic tweaks, focusing on color schemes and label placements for enhanced readability and visual appeal.
[15:00-20:00] Experimentation with various chart types, including adjustments based on specific data visualization goals and prompt modifications.
[20:00-25:00] Exploration of additional Matplotlib features, such as different chart styles and layouts, to showcase the custom GPT's capabilities.
[25:00-28:00] Final thoughts on the utility of custom GPTs in data visualization, emphasizing their adaptability and the potential for learning and customization.

[MicahJohns]

Like this comment if you guys want more videos like this.

[MicahJohns]

[0:00-0:30] Introduction to using custom GPTs for creating Matplotlib visuals in Python.
[0:31-1:00] Demonstrating how to generate a line chart showing sales trends using voice commands.
[1:01-1:30] The GPT generates a visual chart and corresponding Python code based on the given prompt.
[1:31-2:00] Requesting a scatter plot of the top salespeople in each division with labeled data points.
[2:01-2:30] Analyzing the generated scatter plot and tweaking prompts for better visualization.
[2:31-3:00] Requesting a visualization of historical figures like Thomas Edison and Elvis Presley.
[3:01-3:30] Creating a histogram for average car sales prices across different sales divisions.
[3:31-4:00] Adjusting histogram settings and discussing the iterative process for desired outputs.
[4:01-4:30] Commanding the GPT to generate a bar plot and refining it for clarity and readability.
[4:31-5:00] Requesting a box plot to analyze differences in sales volumes between divisions.
[5:01-5:30] Iteratively refining the visualization and exploring alternative chart types for better representation.
[5:31-6:00] Adjusting the chart's color scheme and labels for improved legibility and aesthetics.
[6:01-6:30] Introduction to creating a stacked area chart for division sales throughout the year.
[6:31-7:00] Adjusting labels and refining the visualization of the stacked area chart.
[7:01-7:30] Requesting changes to the labels and background color for better clarity.
[7:31-8:00] Creating a paired bar chart for division sales comparison.
[8:01-8:30] Refining the bar chart to improve visualization and readability.
[8:31-9:00] Exploring a box plot to analyze differences in sales volumes between divisions.
[9:01-9:30] Iterative process of refining the box plot for better representation of data.
[9:31-10:00] Requesting a variety of data visualizations using map plot lib options.
[10:01-10:30] Analysis of different visualization outputs including heat maps and pie charts.
[10:31-11:00] Discussing the utility of custom GPTs in generating insightful data visualizations.
[11:00-15:00] Refinement of charts with aesthetic tweaks, focusing on color schemes and label placements for enhanced readability and visual appeal.
[15:00-20:00] Experimentation with various chart types, including adjustments based on specific data visualization goals and prompt modifications.
[20:00-25:00] Exploration of additional Matplotlib features, such as different chart styles and layouts, to showcase the custom GPT's capabilities.
[25:00-28:00] Final thoughts on the utility of custom GPTs in data visualization, emphasizing their adaptability and the potential for learning and customization.