如何系统学习机器学习?

想要成为一位流工智能从业者?体系进修机械进修是重点!

机械进修是一门没有需求停止明白编程就能使盘算机施展感化的迷信。正在已往的十年里,机械进修已为咱们供应了主动驾驶汽车、及时语音识别、高效收集搜寻等有用对于象,并资助咱们极年夜地提拔了对于人类基因组的认知。很多钻研职员都觉患上倒退机械进修是向人类程度的野生智能迈进的最好形式。

这里向人人供应三个体系进修机械进修的步骤:进修根基编码学问、进修机械进修及深度进修、专一于一个脚色。

想要胜利构建机械进修体系,基础的编程妙技是先决前提。正在最先实际容易的机械进修算法以前,你需求具有编写一个容易的盘算机顺序(函数挪用,for loops,前提语句,基础的数学操作)的威力。尽管控制更少数学学问能让你更具上风,但也没有势必精神过量投入到诸如线性代数、多少率以及统计如许的数学根基上。

正在进修了根基编码学问后,就能够正式最先你的机械进修之旅了。由斯坦福年夜学推出的“机械进修课程”是你没有错的抉择。该课程供应了对于机械进修、数据开掘以及统计形式识其余宽泛引见,能资助人人有用构建对于机械进修的认知以及明白。重要内容囊括:监视进修、无监视进修以及机械进修的最好实际。

该课程从少量的案例钻研以及运用中吸收教训,便于人人进修怎样将进修算法运用于构建智能机械人(感知、管制)、文本明白(收集搜寻、反渣滓邮件)、盘算机视觉等工作。

另外,深度进修也是你需求浏览的畛域。由 DeepLearning.AI 开辟的“深度进修业余课程”涵盖了你正在盘算机视觉、做作言语解决以及语音识别等畛域构建运用顺序所需的学问。你将从医疗保健、主动驾驶、手语浏览、音乐天生以及做作言语解决等方面发展案例钻研,以便于正在控制实践学问的根基上相识深度进修正在各行业中的实际运用。

当你对于机械进修以及深度进修都有了较为深切的进修后,下一步言论将取决于你心中想要成为的脚色,例如成为数据迷信家、机械进修工程师或者机械进修钻研员等,亦或者是将所学的 AI 妙技与你现正在处置的事情相联合,将野生智能更好地运用于事实天下题目。

肯定脚色以后就要迈入真正的实际关键了。对于此,名目抉择以及团队合作相当重要。肯定可行以及有代价的名目是一个重要的步骤,必须正在你的职业生涯中重复实际。正在实现名目标历程中,团队合作比单打独斗更容易获患上胜利。与他人合作、供应及听取发起的威力相当重要,这能资助你正在合作历程中建设宽泛的瓜葛网。当你需求资助或者发起的时刻,领有一个壮年夜的职业瓜葛网可能助你前行。

正在累积了未必的机械进修实践学问以及实际教训后,找到一份相干的事情看似是每一集体的终究宗旨,但它只是冗长职业生涯中的一小步。你需求维持自律,一直进修。身旁的人并没有清晰你把周末的光阴是用正在进修照样刷手机上了,但跟着光阴的推移,他们终将细致赴任异。自律的生涯能够资助你正在维持衰弱的同时连续提高。

指望上述发起能为你关上机械进修的年夜门,从初学者一起走向从业者。这条路注定是宽绰却没有平整的,但这一起上碰到的人以及事都将助你走向胜利。

请连续进修!

吴恩达

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 progra妹妹ing. 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 progra妹妹ing 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 co妹妹it 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

 

觉得好的话记得打赏赞助小灰灰哦,小灰灰灰更有动力的,谢谢

小灰灰

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