This blog documents my learning journey in the Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications from Texas McCombs, The University of Texas at Austin. The course covers a wide spectrum of AI and ML topics, including Python Foundations, Machine Learning (Basic and Advanced), Neural Networks, NLP with Generative AI, Computer Vision, Model Deployment, Multi-modal Generative AI, Statistical Learning, and Recommendation Systems. Each module is designed to provide hands-on experience and practical insights into building intelligent business solutions using modern AI/ML techniques.
Learn more about the program here
Module 1 : Python for AI & ML
In this module, which spanned around 6 weeks, I’ve explored the core building blocks of Python for Artificial Intelligence and Machine Learning - from refreshing my knowledge on Python syntax and data structures, to analyzing real datasets using Pandas and visualizing insights through Exploratory Data Analysis (EDA).
This phase of learning laid a strong foundation for my upcoming AI/ML modules, helping me gain practical, hands-on experience with essential Python libraries and techniques.
Summary of What I’ve Learned
| S.No | Topic | What I Learned | Blog Link |
|---|---|---|---|
| 1 | Python Foundations for AI & ML | Learned Python basics — syntax, data types, loops, functions, conditionals, and the importance of Python as the foundation for AI/ML. Explored how Python simplifies data handling and algorithm prototyping. | (Coming soon) |
| 2 | Python – NumPy | Gained understanding of arrays, vectorization, broadcasting, and mathematical operations. Practiced working with real numeric data efficiently using numpy arrays. |
Python – NumPy |
| 3 | Python – Pandas | Learned to create and manipulate DataFrames and Series. Explored data indexing, filtering, grouping, joining, and visualization. Hands-on exercises on combining and cleaning data. |
Python – Pandas |
| 4 | Exploratory Data Analysis (EDA) | Understood how to summarize and visualize datasets using Pandas, Matplotlib, and Seaborn. Learned techniques like univariate, bivariate, and multivariate analysis, detecting outliers, and treating missing data. | Python – EDA |
| 5 | Analyzing Text Data | Discovered how to process and analyze unstructured text using Python. Learned key NLP steps like text cleaning, tokenization, stemming, and vectorization (Bag of Words, N-grams). Practiced sentiment analysis and lexicon-based approaches. | Python - Analyzing Text Data |
Reflections from Module 1
Through this first module, I’ve not only strengthened my Python programming fundamentals but also built the ability to translate raw data into structured insights - a skill essential for AI and ML workflows.
From manipulating structured data with Pandas to analyzing text sentiment, every step has brought me closer to understanding how data powers intelligent systems.
This marks the completion of my Python Foundations track - setting the stage for more advanced modules in Machine Learning and AI ahead!
Module 2 : Machine Learning
(In Progress)
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