机器学习 深度学习 ai
STRATEGY
战略
Learn theory + practical aspects.
学习理论和实践方面的知识。
(At first get an overview of what you are going to learn).
(首先获得要学习的内容的概述)。
Gain a good hold/insight on each concept.
掌握/理解每个概念。
If you arenot comfortable with mathsat first; just get yourself comfortable with why we needed that maths part, and what is its O/P. Then, come tounderstand it later. Never skipany concept forever.
如果您刚开始对数学不满意，可以使用只是让自己对我们为什么需要数学部分以及它的O / P感到满意。 然后，稍后再了解。永远不要跳过任何概念。
PRACTICE, PRACTICE, PRACTICE and PRACTICE!!!
实践，实践，实践和实践！！！
(
(
Codingcomes into this phase)
编码进入此阶段)
Understandboundarycases andfailureconcepts, to grap the concept of that topic.
了解边界情况和故障概念，以掌握该主题的概念。
BELIEVE! Itseasy;
相信！ 它很简单;
Total of 150+ hours is good enough (5-10 hrs for 3-6 months)
总共150个小时以上就足够了(3-6个月5-10个小时)
SPECIAL TIPS
特别提示
For those facing difficulty in maths (like me :))
对于那些面临数学困难的人(像我一样：)
You need to consider math as
您需要考虑数学
Poetry + Art.
诗歌+艺术。
Eqns:- Read in English sentence → Poetry
Eqns：-用英语阅读→诗歌
Geometry:-Visualize (human–visualizing creature) → Art
几何：可视化(人类可视化的生物)→艺术
Steps and Guidelines
步骤和准则
You should note that, this is not the only way to approach for learning ML/DL. But this is really one of thebest resource listfor ML. You may have an option to pursue any certification course of your choice. It’s also good. I don’t discourage you for that. But, In case you want to save your money or you want to give ML a try and don’t want your money wasted in case you can’t continue. Then, you must follow some free available stuff online. And, trust me; you can never get a list better than this one. I have narrowed everything so precise so that you don’t get distracted elsewhere.
您应该注意，这不是学习ML / DL的唯一方法。 但这确实是ML最好的资源列表之一。 您可以选择继续自己选择的任何认证课程。 也不错 我不劝阻你。 但是，如果您想省钱，或者想尝试一下ML，并且不想浪费您的钱，以防万一您无法继续下去。 然后，您必须在线关注一些免费的可用内容。 而且，请相信我； 您再也找不到比这更好的清单了。 我已经将所有内容缩小到如此精确的程度，以使您不会在其他地方分心。
1) Programming language (Python or R)
1)编程语言(Python或R)
2) Probability and statistics
2)概率统计
3) Linear Algebra
3)线性代数
Online course - Linear Algebras; Khan Academy
在线课程-线性代数； 可汗学院
4) Calculus & Numeric Optimization
4)微积分与数值优化
5) Brief of Machine learning
5)机器学习简介
Note:At this stage, I would like to personally recommend you a free available online course: Machine Learning @ Kaggle | Learn
- This will give you a basic to intermediate level of understanding in ML. Plus; you would learn How to compete at different platform like Kaggle, or Hackerearth.
注意：在此阶段，我个人想向您推荐免费的在线课程： 机器学习@ Kaggle | 学习
-这将使您对ML有了基本到中级的理解。 加; 您将学习如何在Kaggle或Hackerearth等不同平台上竞争。
6) Classification and Regression technique
6)分类与回归技术
7) Clustering Techniques
7)聚类技术
Same as above (6)
YouTube - Clustering techniques; UB Vzard
同上(6)
YouTube-群集技术； UB Vzard
8) Dimensionality Reduction
8)降维
Same as above
YouTube - Dimensionality Reduction Techniques; UB Vzard
同上
YouTube-降维技术； UB Vzard
9) Neural networks and deep learning
9)神经网络和深度学习
Online courses
在线课程
Deep learning; Kaggle | Learn; Dan.S.Becker
深度学习； Kaggle | 学习; 丹·贝克尔
Deep Learning, Andrew Ng; Course era/YouTube
深度学习，吴安德； 课程时代/ YouTube
Convolution Neural Networks; Stanford online/ YouTube (CS231n) (*If you want specifically CNN at broader scale.)
卷积神经网络 斯坦福在线/ YouTube(CS231n)(*如果您想更广泛地专门使用CNN。)
Deep Learning A-ZTM; Udemy
深度学习AZ TM ; 乌迪米
U B Vzard
UB Vzard
10) Problem solving
10)解决问题
- solve problems end to end
-端到端解决问题
- Participate in contests
-参加比赛
- compete in Data Hacks and Student Data fest
-参与数据黑客和学生数据节
Understand why a technique is working (or) not working
了解为什么某项技术有效(或无效)
Document /code (GitHub or blog)
文档/代码(GitHub或博客)
Portfolio of 5 or more case studies
5个或更多案例研究的组合
Read other’s blog or code
阅读他人的博客或代码
11) Youtube series – UB Vzard
11)Youtube系列– UB Vzard
12) LinkedIn– Get in touch with Data Science community professionals. They will Help you, guide you and most importantly motivate you.
12)LinkedIn–与数据科学社区专业人士联系。 他们会帮助您，指导您，最重要的是激励您。
Note:Of course; this awesome article series @IncludeHelp. Stay tuned for totally aligned and simplest platform for insightful knowledge at ease.
注意：当然可以； 这个很棒的文章系列@IncludeHelp。 敬请关注完全一致且最简单的平台，以轻松获取有见地的知识。
Conclusion
结论
At last, I would like to conclude that, don’t waste your crucial time wasting behind finding learning resources; although this is important before getting started. This bucket is really helpful and good enough to get you from Beginner to Advance Level. Find and mark out the best one and most suitable for you. And start over as soon as possible. And, always stick to that. You can take references from other resources too. A hearty apology, because U B Vzard is active on YouTube but it has not contained any ML videos yet; But, I am working on it with a leopard speed. You will have them ASAP. Don’t lose your hope. Trust me, it is easy. Catch you later in the next article. HAPPY LEARNING!
最后，我想得出一个结论，不要浪费您的关键时间来浪费学习资源； 尽管在开始之前这很重要。 这个存储桶确实很有帮助，并且足以使您从初学者升到高级。 找到并标记出最适合您的一种。 并尽快重新开始。 并且，始终坚持这一点。 您也可以从其他资源中获取参考。 致以诚挚的歉意，因为UB Vzard在YouTube上很活跃，但尚未包含任何ML视频； 但是，我正在以更快的速度进行开发。 您将尽快拥有它们。 不要失去希望。 相信我，这很容易。 下一篇文章稍后会吸引您。 快乐的学习！
翻译自: /ml-ai/how-to-learn-machine-learning-and-artificial-intelligence.aspx
机器学习 深度学习 ai
