Starting something new is never easy, especially in a field as broad and fast-evolving as Data Science and Machine Learning. I began my journey with curiosity and a clear goal: to understand how data can be transformed into meaningful insights. I started by learning Python and working with libraries like Pandas and NumPy, practicing data analysis on small datasets. At first, cleaning data and debugging errors felt challenging, but I learned that mistakes are an essential part of growth.Currently, I am focusing on exploratory data analysis, beginner machine learning models, and real-world datasets from Kaggle. I enjoy breaking problems into small steps and learning by building projects rather than relying only on tutorials. This blog documents my learning in public, where I share lessons, c…
Starting something new is never easy, especially in a field as broad and fast-evolving as Data Science and Machine Learning. I began my journey with curiosity and a clear goal: to understand how data can be transformed into meaningful insights. I started by learning Python and working with libraries like Pandas and NumPy, practicing data analysis on small datasets. At first, cleaning data and debugging errors felt challenging, but I learned that mistakes are an essential part of growth.Currently, I am focusing on exploratory data analysis, beginner machine learning models, and real-world datasets from Kaggle. I enjoy breaking problems into small steps and learning by building projects rather than relying only on tutorials. This blog documents my learning in public, where I share lessons, challenges, and progress as I grow in Data Science.