Amar Harolikar’s Post

🍓 GenAI supports creativity. With JupyterAI you get to bring in your creative/critical thinking skills, without going into the weeds of technical steps. Many steps you take today, will be a thing of the past in next 3-5 years. Train your mind to be a critical thinker. Be solid at technical stuff. However, don’t let define a specific coding language or a library your limits of ingenuity. 👉 Hugging Face Spaces Demo of Jupyter AI Agent: https://lnkd.in/gdtUHnJu #genai #jupyter #ai #ml #python #datascience

🤩 Really impressive and convenient agent in a Jupyter notebook. I told it to create a neural network in PyTorch to predict the next time step of the Lotka-Volterra data from the example, and failed. But, the completion is quite good. It seems useful for simple, introductory level, and repetitive tasks. Bravo HF! Code: https://lnkd.in/dp_gJmWk There are more agents for Jupyter notebooks: https://lnkd.in/d96qPhWw https://lnkd.in/d4AX7g33

Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon! https://lnkd.in/eGmyXh-q

Just completed the Introduction to Data Visualization with Matplotlib. Onto Seaborn next

Data visualization is the art of transforming complex data into easily understandable formats. Effective dashboards leverage this art to present insights in a clear and actionable way. But what tools do data visualization wizards use to create these masterpieces?

Through effective visualizations, we can identify patterns, detect anomalies, and gain insights that inform our modeling decisions. Whether it's a simple scatter plot, a complex heatmap, or an interactive dashboard, the right visual representation can make all the difference in understanding our data and communicating our findings to stakeholders. Investing time in data visualization enhances the interpretability of your results and helps you make informed decisions throughout the machine learning pipeline. Embrace the power of visuals to tell the story of your data!

Introduction to Data Visualization with Matplotlib

In this task, you will be working with the Pandas and M atplotlib libraries to visualize data from a DataFrame. The goal is to create both a bar chart and a line chart t o represent different aspects of the data. You will nee d to customize each chart by adding relevant labels fo r the axes, a descriptive title, and a legend to differenti ate the data series. This exercise will help you underst and how to create and customize visualizations in Pyt hon to effectively communicate data insights." # mainflow Mainflow

I’ve just completed the Introduction to Data Visualization with Matplotlib Course on DataCamp!