作者：吳恩達
連結：https://www.zhihu.com/question/266291909/answer/2429781356
來源：知乎
Do you want to become an AI professional? The key to machine learning mastery is to approach your learning systematically!

Machine learning is the science of making a computer perform work without explicit programming. In the past decade, machine learning has enabled utilities such as self-driving cars, real-time speech recognition, efficient web search, and boosting our knowledge of the human genome. Many researchers believe that machine learning promises the greatest possibility in realizing human-level AI.

Here, I‘d like to share three steps to learn machine learning in a systematic way:
First, you should learn coding basics. Second, you should study machine learning and deep learning. Third, you should focus on the role you would like to have.
Fundamental programming skills are a prerequisite for building machine learning systems. You will need to be able to write a simple computer program (function calls, for loops, conditional statements, basic mathematical operations) before you can start implementing preliminary machine learning algorithms. Knowing more math can give you an edge, but it won’t be necessary to spend much time on specific mathematical issues such as linear algebra, probability and statistics.

Having gained some fundamental coding skills, you can officially begin your journey of machine learning. My Machine Learning course from Stanford University is a great choice. It provides a general introduction to machine learning, data mining, and the statistical approach of pattern recognition. The course will also help you to develop your practical understanding of how to use machine learning in the real world. For instance, when to use supervised learning, unsupervised learning, and machine learning. The machine learning course draws insights from numerous case studies and applications. It is suitable for learning how to apply algorithms to a wide-variety of tasks, such as intelligent robots building (perception, control), natural language understanding (web search, anti-spam emails), computer vision (identifying diseases in medical imagery, finding defects in manufacturing), and much more.

Deep learning is a subset of machine learning that is growing more important, and is worth your attention as well. It uses neural networks to make powerful predictions, and is the driving force behind many of today’s most exciting technologies. For example, self-driving cars, advanced web search, and face recognition all use deep learning. The Deep Learning Specialization, developed by DeepLearning.AI, covers the knowledge you need to build deep learning applications in fields such as computer vision, natural language processing, and speech recognition. You will conduct case studies in healthcare, autonomous driving, sign language reading, music creation, and natural language processing, so you can familiarize yourself with the practical application of deep learning in various industries while mastering theoretical knowledge at the same time.

Once you have learned the foundations of machine learning and deep learning, the next move depends on the role you have in mind. For example, do you want to be a data scientist, engineer, or machine learning researcher? Or, do you consider developing AI skills to complement your existing expertise? If so, you can learn AI as a way to better apply your expertise to real-world problems.

After deciding the role, it's time to move on to real practice. You’ll want to get experience working on projects and as a part of a team. Identifying viable and valuable projects is an important skill, and it’s one that you’ll continue to develop throughout your career. The best way to start is to volunteer to help with other peoples’ projects. Eventually you will develop the confidence and experience to lead your own. For completing a project, teamwork is more likely to succeed than solo effort. It is critical to have the ability to collaborate with others, give and take advice, as this helps you build connections. Teamwork also helps you build out your network of professional connections. You can call on people who you have worked with in the past to provide advice and support as you move through your career.

The ultimate goal.of course, is to find a job in machine learning. This will come after you have acquired both theoretical knowledge as well as practical experience. When looking for a job, don’t be shy about reaching out to people you have met while taking courses or working on projects. You can also connect directly with professionals who are already working in the field. Many of them are happy to act as your mentor. Finding your first job, however, is a small step in a long-term career. It is important to cultivate self-discipline and commit to constant learning. People around you may not be able to tell whether you spend your weekends studying or on your smartphone, but day by day, and year over year, it will make a difference. Discipline ensures that you move forward while staying healthy.

I hope these suggestions could open the door to machine learning and help get you job-ready. The journey ahead will surely be a bumpy one, but rest assured that what you encounter along the way will help you succeed.

By the way, courses from DeepLearning.AI will be available on Zhihu soon. Stay tuned and see you next time!

Keep Learning!

Andrew

想要成為一名人工智慧從業者？系統學習機器學習是重點！

機器學習是一門不需要進行明確程式設計就能使計算機發揮作用的科學。在過去的十年裡，機器學習已經為我們提供了自動駕駛汽車、實時語音識別、高效網路搜尋等實用工具，並幫助我們極大地提升了對人類基因組的認知。許多研究人員都認為發展機器學習是向人類水平的人工智慧邁進的最好方式。

這裡向大家提供三個系統學習機器學習的步驟：學習基礎編碼知識、學習機器學習及深度學習、專注於一個角色。

想要成功構建機器學習系統，基本的程式設計技能是先決條件。在開始實踐簡單的機器學習演算法之前，你需要具備編寫一個簡單的計算機程式(函式呼叫，for loops，條件語句，基本的數學操作)的能力。雖然掌握更多數學知識能讓你更具優勢，但也不必將精力過多投入到諸如線性代數、概率和統計這樣的數學基礎上。

在學習了基礎編碼知識後，就可以正式開始你的機器學習之旅了。由斯坦福大學推出的“機器學習課程”是你不錯的選擇。該課程提供了對機器學習、資料探勘和統計模式識別的廣泛介紹，能幫助大家有效構建對機器學習的認知和理解。主要內容包括：監督學習、無監督學習和機器學習的最佳實踐。該課程從大量的案例研究和應用中汲取經驗，便於大家學習如何將學習演算法應用於構建智慧機器人(感知、控制)、文字理解(網路搜尋、反垃圾郵件)、計算機視覺等任務。

此外，深度學習也是你需要涉獵的領域。由DeepLearning.AI開發的“深度學習專業課程”涵蓋了你在計算機視覺、自然語言處理和語音識別等領域構建應用程式所需的知識。你將從醫療保健、自動駕駛、手語閱讀、音樂生成和自然語言處理等方面開展案例研究，以便於在掌握理論知識的基礎上了解深度學習在各行業中的實際應用。

當你對機器學習和深度學習都有了較為深入的學習後，下一步行動將取決於你心中想要成為的角色，例如成為資料科學家、機器學習工程師或機器學習研究員等，亦或是將所學的AI技能與你目前從事的工作相結合，將人工智慧更好地應用於現實世界問題。

確定角色之後就要邁入真正的實踐環節了。對此，專案選擇和團隊合作至關重要。確定可行和有價值的專案是一個重要的步驟，必須在你的職業生涯中反覆實踐。在完成專案的過程中，團隊合作比單打獨鬥更容易取得成功。與他人合作、提供及聽取建議的能力至關重要，這能幫助你在協作過程中建立廣泛的關係網。當你需要幫助或建議的時候，擁有一個強大的職業關係網能夠助你前行。

在積累了一定的機器學習理論知識和實踐經驗後，找到一份相關的工作看似是每個人的最終目標，但它只是漫長職業生涯中的一小步。你需要保持自律，不斷學習。身邊的人並不清楚你把週末的時間是用在學習還是刷手機上了，但隨著時間的推移，他們終將注意到差異。自律的生活可以幫助你在保持健康的同時繼續進步。

希望上述建議能為你開啟機器學習的大門，從初學者一路走向從業者。這條路註定是寬闊卻不平坦的，但這一路上遇到的人和事都將助你走向成功。

我的深度學習相關課程也將在近期登入知乎，敬請關注，我們下次再見！

請繼續學習！

吳恩達

釋出於 2022-04-08 18:38