AI 与机器学习developers.google.com编辑精选
机器学习速成课程
Machine Learning Crash Course
2018 — 2019，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
Google 官方推出的机器学习速成课程，免费提供视频、练习和 TensorFlow 实战内容，涵盖特征工程等入门主题，并支持多语言翻译。它面向希望快速了解 ML 的开发者，是 2018 年前后最流行的入门资源之一。
为什么经久不衰：由 Google 官方维护、内容持续更新，是零基础进入机器学习领域最稳妥的免费起点之一。
HN 评论精选
速成课适合入门概览，但真正的学习要靠在真实数据上动手实践
看完课程涵盖的主题，标准的 AI 课程注意事项都适用。这类 MOOC 能很好地教你工具，Keras 等新工具也让这个领域更容易入门——我很反感那种'没有博士/5 年研究经验就别碰 ML'的过时门槛。但与那些流行文章相反，上完速成课并不意味着你就是专家。它们很适合了解技术全貌，但没有什么比在真实、嘈杂的数据集上动手应用工具、解决过程中不断出现的小问题更有效。在我看来，阅读 Keras 文档和示例比任何 MOOC 都更能教会你 AI/ML。（当然 Keras 现在是 TensorFlow 的一部分，所以这个速成课和 Google 形成了漂亮的垂直整合！）
minimaxir查看英文原文
Looking through the topics covered, the standard AI-course caveats (https://news.ycombinator.com/item?id=16247629) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. However, contrary to the thought pieces that tend to pop up, taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats applying the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process. Reviewing the Keras documentation (https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion. (Of course, Keras is now a part of TensorFlow, so there's a neat Google vertical intergration with this crash course!)
课程对特征工程的讲解很有价值，这是初学者最常卡住的关键环节
很高兴看到他们有一个很好的特征工程入门章节！特征工程往往是提升模型质量最有效的方法，也是新手（甚至专家）最容易卡住的地方。Google 演示了如何处理 json 文件和分类变量。如果有人想深入，我在做一个用于自动化特征工程的开源 Python 库 Featuretools，适合多表等复杂数据场景。
kmax12查看英文原文
Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/representation/feature-engineering. If anyone is looking to get more indepth, I work on an open source python library for automated feature engineering called Featuretools https://github.com/featuretools/featuretools/. It can help when your data is more complex such as when it comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos.
推荐另一个深度学习入门资源：无需博士学位的 TensorFlow 讲座
对想深入学习深度学习的人，'Tensorflow and deep learning - without a PhD' 非常好，一个 2 小时的演讲覆盖了大量内容。
eddieplan9查看英文原文
For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk. https://www.youtube.com/watch?v=vq2nnJ4g6N0
对比 Andrew Ng 课程：先学无框架的基础再上框架是否更好
这个课程看起来做得很用心，也是学 TensorFlow 的好方式。有人在上 Andrew Ng 的机器学习课吗？我进行到一半很喜欢，尤其欣赏编程练习用 MatLab/Octave 完成，让我不被 API 干扰地理解基础、培养直觉。显然框架是生产环境 ML 的正道，但我想知道这种自底向上的方法是否可取，还是会在之后转向 Keras/TensorFlow 时产生误导？
andyjohnson0查看英文原文
This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future. Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fundamentals without an API getting in the way, and developing some good intuition. Obviously frameworks are the way to go for production ML work, but I wonder whether ML people here think this bottom-up approach is advisable or could it be misleading when I move on to Keras/TensorFlow/whatever? [1] https://www.coursera.org/learn/machine-learning Edit: brevity
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