Chinese-American computer scientist and statistician, Andrew Ng is one of the most popular researchers among the millennials for his work in artificial intelligence, machine learning, deep learning, and other emerging technologies. His online courses on Coursera and deeplearning.ai has helped many enthusiasts to democratise these emerging technologies. 

In one of the webinars on building a career in machine learning, Ng shares tips and tricks on how to break into AI and discussed a few valuable skills that a person must have in order to successfully switch career machine learning. 

Ng had earlier tweeted, “I often advise people to take on projects you’re only 70% qualified for, but then learn like crazy to bridge that 30%.” 

On this note, we listed down eight most important points that the deep learning master advised in his video on building a career with machine learning. 

1| Understand Emerging Tech

If someone wants to pursue a career with emerging technologies, it is very important for him/her to understand the basics of machine learning, artificial intelligence, deep learning, graphical models, neural networks and other technologies. Currently, the organisations are shifting towards the ecosystem where techniques like reinforcement learning, LSTM, CNN, RNN, etc. have been used thoroughly. Programming languages like Python, R, SQL, etc. are demanding these days and one must have a clear concept of these programming languages. The better way is to keep updated as much as possible. 

2| Learn From Research Papers

This is the most important point that Andrew Ng keeps stressing in almost all his videos. Whether it be a career-building webinar or a Stanford University online deep learning class, Ng advised to all the learners and listeners to read at least two research papers on the merging technologies. According to him, it turns out to be a very efficient way to learn the depth of any knowledge regarding the emerging tech. 

3| Course Work And MOOCs

Massive Open Online Courses (MOOC) and course work provided by organisations and academia contains a massive amount of information which cannot be found anywhere else. These sources contain exercises, practicals, etc. which helps a candidate not just to understand the topic but where and when to imply it. It is an efficient way to grab depth of knowledge in the interested areas, To be a strong potential candidate, completing online course work and MOOCs and adding it to the resume surely create a stand out among the other candidates in a job interview.

4| Working in a research project

Doing an internship allows a practical depth of knowledge which allows a candidate to demonstrate the skills. Not only internships but also taking up a machine learning project on its own and trying to build and develop a model provides in-depth knowledge to the domain where the candidate wants to work on. 

5| How to Build ML Systems

Learning how to make machine learning systems work is very crucial in this field. With the help of online courses available, one can learn how to build a machine learning system from scratch. This will help in fetching a good-paid job along with a fruitful career in machine learning. 

6| Prepare for ML Questions Along With Demonstrating the Portfolio of Work 

While appearing for a machine learning job interview, one must prepare the questions that are related to machine learning and artificial intelligence. There are various blogs where one can find common interview questions on emerging technologies. Also, in an interview, when a candidate is asked questions on topics like machine learning, along with answering the question, s/he must also demonstrate the portfolio of the work that has been done earlier with these technologies.

7| Importance of Dirty Work

According to Ng, downloading dataset, cleaning, plotting the learning curve and trying to figure out whether it is right or wrong, working and predicting PCAs can be said as the dirty work. However, he also mentioned that these are the most important parts while building a machine learning model. After all, data which is fed decides the fate of a machine learning model. One should not be afraid of jumping into doing dirty work. 

8| Lifelong Learner

Read research papers regularly or at least a few every week. The secret to becoming good at machine learning is not just studying certainly any weekend but to keep the pace by learning every weekend. One must study online courses and keep finding interesting research papers. If someone studies two papers a week it will make him/her read 100 papers in a year which is eventually a huge amount of knowledge. This will help in getting better in AI skills with time. The current job market is directly proportionate to the actual job skills in the present scenario and constant learning will prove to be a benefit in this case.