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。
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!