Beginner Resources for Machine Learning: Languages, Books, Videos, Blogs, Competitions, and Papers

This article compiles a comprehensive set of beginner‑friendly resources for learning machine learning, covering programming languages, books, video courses, blogs, competition platforms, and research papers.

Language

Python is the dominant language for machine learning, supported by numerous libraries:

numpy : fundamental package for numerical computing – http://www.numpy.org/

pandas : data manipulation – http://t.cn/EJ2Mx7X

scipy : scientific algorithms – http://t.cn/RifbS3x

scikit-learn : classic ML algorithms and utilities – https://scikit-learn.org/

scikit-multilearn : multi‑label learning – http://scikit.ml/

keras : beginner‑friendly deep‑learning library – https://keras.io/zh/

Other deep‑learning frameworks such as TensorFlow and PyTorch are also mentioned.

Books

《统计学习方法》 (Statistical Learning Method) by Li Hang – a classic for understanding algorithm details.

《机器学习》 (Machine Learning) by Zhou Zhihua – also known as the "Watermelon Book," covering all major sub‑fields.

《推荐系统实战》 (Practical Recommender Systems) by Xiang Liang – useful for recommendation topics.

《概率论与数理统计》 – foundational probability and statistics.

Pattern Recognition and Machine Learning (PRML) – classic English text.

Reinforcement Learning: An Introduction – entry‑level RL book.

PDF versions of the above books are available at: http://t.cn/EJ2yUcr

Videos

Andrew Ng's Coursera/NetEase courses – basic introductory lectures. Links: NetEase http://t.cn/RwUWKMS , Coursera http://t.cn/RJZQbV2

Prof. Li Hongyi's courses – highly recommended. Organized version: http://t.cn/RueztdS

Blogs

Domestic :

Flickering (Tencent engineers) – http://www.flickering.cn/

Meituan tech blog – https://tech.meituan.com/

Su Jianlin's blog – https://spaces.ac.cn/

Other platforms: CSDN, Zhihu, Jianshu, etc.

International :

Netflix Tech Blog – https://medium.com/netflix-techblog

Towards Data Science – https://towardsdatascience.com/

GitHub – source code repository.

Competitions

Participating in competitions helps validate learning and apply algorithms to real‑world problems.

Major Chinese platforms: Tianchi ( https://tianchi.aliyun.com/home/ ), DataCastle, DataFountain, Biendata, KESCI, JData.

International platform: Kaggle ( https://www.kaggle.com/ ).

Papers & Conferences

Key conferences for machine learning and AI research include:

Data mining: SIGKDD (links to accepted papers for 2016‑2018).

Recommendation systems: SIGIR (links to accepted papers for 2016‑2018).

Machine learning: AAAI, IJCAI, ICML, NeurIPS (NIPS) – with links to recent accepted papers.

Other notable venues: CIKM, ECML‑PKDD, ICDM, SDM, WSDM, ACL, EMNLP, NAACL, COLING, AISTATS, CVPR, ICCV, ECCV.

Readers are encouraged to explore papers relevant to their interests and consider improvements on existing methods.

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