Chapter 9: AI Safety, Security & Responsible AI
As we build more powerful AI applications, we need to answer an equally important question: how do we build AI
OpenAI & GenAI Development
OpenAI & GenAI Development
Chapter 5: Function Calling & Tools
In previous chapters, our AI applications mostly followed this pattern: But real business applications need to perform actions, not just
OpenAI & GenAI Development
Chapter 1: OpenAI & Generative AI Fundamentals
Welcome to the first chapter of the GenAI Developer Training Program. Before we write a single line of AI code,
Data Analysis Using Python
Tutorial 0: Setting Up Your Machine for Python Data Analysis
Introduction Before diving into any of the practicals in this series — creating DataFrames, cleaning data, building charts, or running
Data Analysis Using Python
Tutorial 10: Advanced Data Cleaning and Transformation with Pandas
Introduction In real-world projects, data is rarely clean. You’ll routinely encounter the same underlying value written in multiple inconsistent ways:
Data Analysis Using Python
Tutorial 9: Working with Dates and Time Series Data in Pandas
Introduction Many real-world datasets involve dates and time: sales by day, website traffic by month, stock prices, weather measurements, customer
Data Analysis Using Python
Tutorial 8: Data Aggregation and Grouping with Pandas
Introduction In the previous chapter, we calculated overall statistics like mean, median, and correlation. Now we’ll learn to answer more
Data Analysis Using Python
Tutorial 7: Descriptive Statistics with Python
Introduction Descriptive statistics helps us summarize and understand data using numbers, so we don’t have to examine every individual value
Data Analysis Using Python
Tutorial 6: Exploratory Data Analysis (EDA)
Introduction Exploratory Data Analysis (EDA) is the process of investigating a dataset thoroughly before drawing conclusions or building models. It’s
Data Analysis Using Python
Tutorial 5: Data Visualization with Seaborn
Introduction Seaborn is a Python library built on top of Matplotlib. It makes statistical visualization easier and produces attractive, informative