I studied Physics in college, so I mostly knew the Math. Here's what I used to self-learn: 1. Machine Learning for Absolute Beginners by Oliver Theobald 2. ISLR 3. Machine Learning by Andrew Ng on Coursera 4. Deep Learning by Andrew Ng on Coursera 5. fast.ai 6. Sebastian Raschka's PyTorch book Good Math refreshers: 1. Mathematics…
…really easy. - There are a number of paths you can go from there. Here's what I did. -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week) -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening. -- Machine Learning Specialization by Andrew Ng on Coursera. -- Deep Learning Specialization by Andrew Ng on Coursera. -- fast.ai course. - Learn…
……可以上 Khan Academy，搞清楚自己的薄弱点。查漏补缺。学会高中水平的微积分、线性代数和概率统计。以后你会需要更高阶的微积分和线性代数，但现在还不用。然后试着读一读《极简机器学习》这本书。它没那么重的数学味。接着按部就班地刷 ML 课程就行了。路上缺啥补啥。机器学习需要的数学“套路”，和在学校/大学里拿高分的“套路”可不是一码事……
…be Khan Academy and knowing your gaps. Fill your gaps. Learn HS level Calculus, Linear Algebra, and Statistics. You will need more Calculus and Linear Algebra later. But not now. Then try studying "Machine Learning for Absolute Beginners" book. It not very mathy. Then just keep going through ML courses. Learn what you need on the way. The "way" of math needed in Machine Learning is not the same "way" that brings you scores in school/college…
我大学学的是物理，所以大部分数学我本来就会。这是我自学用过的资料：1. 奥利弗·西奥博尔德的《极简机器学习（Machine Learning for Absolute Beginners）》 2. ISLR 3. 吴恩达在 Coursera 上的机器学习课程 4. 吴恩达在 Coursera 上的深度学习课程 5. fast.ai 6. 塞巴斯蒂安·拉施卡的 PyTorch 书。复习数学的好资料：1. 数学……
I studied Physics in college, so I mostly knew the Math. Here's what I used to self-learn: 1. Machine Learning for Absolute Beginners by Oliver Theobald 2. ISLR 3. Machine Learning by Andrew Ng on Coursera 4. Deep Learning by Andrew Ng on Coursera 5. fast.ai 6. Sebastian Raschka's PyTorch book Good Math refreshers: 1. Mathematics…
……真的很容易。——从那里开始有好几条路可以走。我当时是这么干的：——IBM 数据科学专业证书（一点都不深入，但全景梳理得很好；花了一周搞定）——奥利弗·西奥博尔德的《极简机器学习》，一晚上就能刷完。——吴恩达在 Coursera 上的机器学习专项课程。——吴恩达在 Coursera 上的深度学习专项课程。——fast.ai 课程。——学……
…really easy. - There are a number of paths you can go from there. Here's what I did. -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week) -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening. -- Machine Learning Specialization by Andrew Ng on Coursera. -- Deep Learning Specialization by Andrew Ng on Coursera. -- fast.ai course. - Learn…
……可以上 Khan Academy，搞清楚自己的薄弱点。查漏补缺。学会高中水平的微积分、线性代数和概率统计。以后你会需要更高阶的微积分和线性代数，但现在还不用。然后试着读一读《极简机器学习》这本书。它没那么重的数学味。接着按部就班地刷 ML 课程就行了。路上缺啥补啥。机器学习需要的数学“套路”，和在学校/大学里拿高分的“套路”可不是一码事……
…be Khan Academy and knowing your gaps. Fill your gaps. Learn HS level Calculus, Linear Algebra, and Statistics. You will need more Calculus and Linear Algebra later. But not now. Then try studying "Machine Learning for Absolute Beginners" book. It not very mathy. Then just keep going through ML courses. Learn what you need on the way. The "way" of math needed in Machine Learning is not the same "way" that brings you scores in school/college…