Master the entire Data Science lifecycle, from Python fundamentals to deploying Large Language Models, using real-world datasets from the African context

Key Features

Wrangle and visualize messy, real-world data using pandas and Seaborn to uncover local insights
Build and deploy predictive Machine Learning models for fintech and agriculture using Scikit-Learn and TensorFlow
Develop Generative AI applications with RAG to answer questions from local legal or medical documents

Book Description

Are you tired of learning Data Science using the same old "Titanic" or "Boston Housing" datasets? Do you want to build AI solutions that actually solve problems relevant to your environment—like predicting mobile money churn, diagnosing crop diseases, or analyzing local market trends?

Python for Data Science and AI: An African Perspective is the definitive guide for the next generation of African technology leaders. Written by industry experts, this book bridges the gap between generic tutorials and the rigorous, engineering-first approach required in the professional world.

This book is an end-to-end journey through the data universe. It begins by assembling a professional toolkit, teaching you to think like an engineer using VS Code, Git, and SQL. You’ll then dive into the art of data wrangling, learning to clean and analyze complex datasets like Mobile Money transaction logs. As you progress, you will master the predictive power of Machine Learning, building systems to identify customer churn and diagnosing cassava leaf diseases using Computer Vision.

The latter parts of the book push you to the cutting edge. You will demystify the Transformer architecture behind GPT-4, learn Prompt Engineering, and build Retrieval-Augmented Generation (RAG) chatbots. Finally, you will tackle MLOps, learning to package your models with Docker and deploy them as scalable APIs in the cloud.

By the end of this book, you will have moved beyond "notebook data science" to building robust, deployed AI systems that solve real problems.

What you will learn

Set up a professional data science environment using Anaconda, VS Code, and Git
Clean, transform, and visualize complex datasets using pandas, Matplotlib, and Seaborn
Train and evaluate Machine Learning models for classification and regression tasks
Build Deep Learning models for Computer Vision to diagnose crop diseases
Create Generative AI applications using Large Language Models (LLMs) and Vector Databases
Deploy your models to production using FastAPI, Docker, and Cloud platforms
Monitor your models for data drift and maintain them using MLOps best practices

Who this book is for

This book is for aspiring data scientists, students, and software developers who want to transition into the fields of AI and Machine Learning. It is particularly designed for learners who want to apply these skills within the African context, solving locally relevant problems. While prior experience with computers is assumed, no previous programming knowledge is required as the book covers Python fundamentals from the ground up.

Wesley Kambale