Jay 发自 凹非寺 量子位 | 公众号 QbitAI Python依然是最好的语言。 在刚刚出炉的IEEE Spectrum 2025编程语言排行榜上，Python再次斩获最受欢迎的编程语言。 不仅是冠军，而且是「一骑绝尘」的断崖式领先—— 第二名Java和Python的差距相当悬殊。 更可怕的是，Python仍保持着迅猛的增长速度，一、二名之间的鸿沟还在越拉越大。 Python十连冠 继连续九届蝉联IEEE Spectrum榜首后，Python今年再次发力，拿下了编程语言综合排名榜十连冠。 而且， 这是Python首次在综合排名、增长速度、就业导向等三个板块都问鼎第一，也是IEEE榜单12年来第一个三冠王。 想当年，Python刚出道时就因简洁性闻名遐迩，因此逐渐替代Java成为了高校的教学语言。 随后，NumPy、SciPy、matplotlib、pandas等库相继问世，让Python迅速成长为科研、金融、数据分析领域的王牌。 在这一过程中， 网络效应发挥了巨大作用。 越来越多的程序员选择Python，并基于它开发了各类工具，在开源社区贡献了海量的代码和解决方案，逐渐构建出属于Python的生态护城 ...

Core Insights - The article outlines an eight-step roadmap for efficiently advancing in AI/ML by focusing on essential skills and avoiding common pitfalls [1]. Group 1: Step-by-Step Learning Path - **Step 1: Master Python Core Libraries** Proficiency in Python is essential for AI/ML, including data cleaning, model building, and result visualization [2]. Key content includes Python basics, advanced AI programming techniques, and libraries like scikit-learn, NumPy, Matplotlib, Seaborn, and Pandas [4]. Recommended resources include CS50 Python course and "Python Data Science Handbook" [4]. Suggested learning period is 3-4 weeks [4]. - **Step 2: Solidify Mathematical Foundations** A strong grasp of linear algebra, probability, and calculus is crucial for understanding models [5]. Key content includes matrix operations, Bayesian thinking, and optimization techniques [5]. Recommended resources include "Linear Algebra" by 3Blue1Brown and MIT's Probability Introduction [5]. Suggested learning period is 4-6 weeks [5]. - **Step 3: Understand Machine Learning Basics** This step is pivotal for transitioning from beginner to competent AI/ML engineer [6]. Key content includes supervised vs. unsupervised learning, reinforcement learning, and deep learning [6]. Recommended resources include Google's Machine Learning Crash Course and "Machine Learning" by Andrew Ng [8]. Suggested learning period is 6-8 weeks [8]. - **Step 4: Hands-On Project Experience** Practical experience through real AI/ML applications is essential for job readiness [9]. Key content includes practical guides and project development [9]. Suggested learning period is ongoing [9]. - **Step 5: Learn MLOps** Understanding MLOps is vital for deploying and maintaining models in real-world scenarios [10]. Key content includes foundational concepts and best practices for model deployment [10]. Suggested learning period is 3-4 weeks [10]. - **Step 6: Specialize in a Domain** After building a foundation, focusing on a specific area like NLP or computer vision enhances employability [11]. Suggested learning period is ongoing [11]. - **Step 7: Stay Updated** Continuous learning is necessary to keep skills relevant in the fast-evolving AI field [12]. Key resources include ArXiv for research papers and notable figures in the field [12]. Suggested learning period is ongoing [12]. - **Step 8: Prepare for Interviews** Comprehensive preparation for interviews is crucial, including explaining model principles and system design [13]. Recommended resources include machine learning interview guides [13]. Suggested learning period is 4-6 weeks [13]. Conclusion - The article emphasizes a structured approach to mastering AI/ML, enabling individuals to transition from novices to job-ready professionals efficiently [1].