Hello everyone, I am back after a very long time. I was busy with my end-semester exams, which went pretty good. You guys know how college exams go. Anyway, this fall I finally started learning about AI and ML since it has become an important part of computer science today.

For a long time, I wondered why AI and ML require NumPy and Pandas. I knew a little about NumPy from our curriculum, mostly the basics like converting data into arrays and performing simple operations. But once I actually started learning AI and ML, I understood the real power behind these libraries.

NumPy is way more than just 1D or 2D arrays. It gives precise control over big data sets and makes complex mathematical tasks easier. Features like random seeding, broadcasting, reshaping, vectorization, and efficient matrix operations changed my perspective completely. Coming from a Java background, starting with C in my first semester, operations like matrix multiplication or elementwise tasks always seemed difficult to operate. These are as simple as working with basic variables. The difference in speed is also huge because NumPy is optimized for numerical computation.

Then I started looking at Pandas. I had never worked with Pandas, and I always thought it was just like NumPy. Sure enough, when I started using it, I found out how powerful this library truly is when working with structured data. Reading files of types CSV, Excel, JSON, or even SQL outputs can easily be done. Functions like head(), tail(), iloc, and loc make filtering or selecting data very intuitive. The describe() method instantly gives you statistical information like mean, count, standard deviation, among others useful in getting a fast feel of a dataset.

Now, I dive into Pandas, where I will go through cleaning, preprocessing, handling missing values, grouping, data aggregation, and transformation. This is all very important because good quality, well-structured data is at the heart of every AI or ML model: a model is only as good as the data quality that it is trained on.

Conclusion

The starting AI and ML made me realize that NumPy and Pandas are not just optional tools, but a must-have for any data worker who wants to do the job efficiently and with meaning. NumPy handles the heavy mathematical lifting while Pandas does the organizing, cleaning, and preparing of data for modeling. Knowing these libraries has ironed my path into AI and ML so much, as well as smoother. I am looking forward to continued learning and exploration of more advanced concepts ahead.