Python for Data Science & Machine Learning Foundations – CG群

AI与ML数据基建实战课

掌握NumPy、Pandas、Matplotlib等核心工具，通过真实非洲数据集（农业、金融等）彻底打通数据清洗与处理的任督二脉，为编写首个ML模型打下坚实基础。适合进阶学习者。

Python for Data Science & Machine Learning Foundations

这是一门专注于 AI 与机器学习（ML）底层数据基建的实战课程。课程直击“多数人学不好机器学习，是因为卡在数据清洗和底座工具上”的痛点。你将通过使用真实的非洲多行业数据集（涵盖农业、金融、公共卫生等），在编写第一个 ML 模型之前，彻底打通数据处理的任督二脉，构建专业数据科学家必备的核心技术栈。

Master NumPy, Pandas, Matplotlib, Scikit-Learn & PyTorch with real African datasets — before your first ML model.

What you’ll learn
⚡ Write clean Python for data science: comprehensions, OOP, file I/O, and *args/**kwargs
⚡ Use NumPy arrays, broadcasting, and vectorisation instead of slow Python loops
⚡ Wrangle messy real-world data using Pandas groupby, merge, and feature engineering
⚡ Produce publication-quality EDA charts with Matplotlib and Seaborn
⚡ Build production-ready Scikit-Learn pipelines that prevent data leakage
⚡ Write a PyTorch training loop from scratch: tensors, autograd, nn.Module, DataLoader
⚡ Apply hypothesis testing and distributions to make better modelling decisions
⚡ Set up a full Colab + Google drive environment for any data science project

Requirements
❗ Basic Python knowledge — you should know what a function, loop, and list is
❗ No prior data science or ML experience needed
❗ A Google account (all work is done in free Google Colab — no local setup required)
❗ Willingness to run real code on real datasets every lesson

Description
Most students fail their first ML course not because the algorithms are hard — but because they can’t read the data, clean it, or understand what the model is operating on.

This course fixes that. You’ll build the exact Python foundation that every professional data scientist uses before touching a single algorithm: NumPy arrays, Pandas wrangling, Matplotlib visualisations, Scikit-Learn pipelines, PyTorch training loops, and statistical thinking.

Every lesson uses real datasets so the skills feel immediately practical, not textbook-abstract.

Every dataset in this course comes from real-world problems — agriculture, finance, and public health — so you’re never practising on made-up numbers. You’ll know how to handle the kind of messy, incomplete, real data that actually shows up on the job.

By the end of this course you will be able to: load any real-world dataset, clean and wrangle it with Pandas, visualise it for EDA, build a full Scikit-Learn preprocessing pipeline, write a PyTorch training loop from scratch, and apply the right statistical test to support your modelling decisions.

This is not a detour from machine learning. This is the ML infrastructure. Students who complete this course go on to finish ML courses — students who skip it simply do not.

Who this course is for
⭐ Python developers who want to transition into data science or ML
⭐ Students who have tried an ML course and felt lost when the data got messy
⭐ Self-taught programmers building a formal data science foundation
⭐ Anyone working with agricultural, financial, or survey data
⭐ Engineers enrolling in the companion ML & Deep Learning course

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