A Practical Guide for Ordinary Programmers to Enter the AI Field

Purpose: Provide a simple, smooth, and implementable learning method to help “ordinary” programmers (bachelor degree, busy work, limited data) enter the AI field from scratch.

AI Overview: AI is not limited to machine learning; historically symbolic logic gave way to statistical machine learning, with deep learning as a prominent subfield.

Learning Method: Define a clear learning goal (enter AI), adopt the principle “interest first, practice combined,” and design a concrete learning plan.

Learning Roadmap: First gain a broad understanding of the domain and cultivate interest, then study machine‑learning fundamentals through a progressive course with hands‑on labs, apply ML to real problems, choose between deep learning or further ML, and finally engage in projects, open‑source contributions, or research.

Stage Details:

Domain Understanding : Know what AI/ML can do and its value.

Knowledge Preparation : Review linear algebra, calculus, probability; improve English; use Google for research.

Machine Learning : Recommended Andrew Ng’s Coursera course; avoid older cs229.

Practical Projects : Build simple projects, preferably in computer vision using OpenCV.

Deep Learning : Resources such as UFLDL, the 2015 Nature deep‑learning paper, “Neural Networks and Deep Learning,” RNN tutorials; avoid overly difficult courses.

Further Machine Learning : Study classic methods like SVM and ensemble learning; recommended books by Zhou Zhihua.

Open‑Source Projects : Explore libraries like DeepLearnToolbox (MATLAB) and TensorFlow.

Conference Papers : Read top conferences CVPR, NeurIPS, etc., for advanced knowledge.

Free Learning : Continue exploring resources based on personal interest.

Conclusion: Successful entry into AI requires solid fundamentals, strong coding ability, a clear learning plan, and sustained interest.