What do you do if you lack assertiveness and confidence in pursuing machine learning career opportunities?

- Strengthen your mathematics knowledge - Strengthen your knowledge on coding and system design. - Read blogs and books - Go through courses and tutorials from Udemy, Coursera or youtube - Try to apply your knowledge into practical exposure through projects using good open-source data

Engage in hands-on projects. It'll allows you to apply ML algorithms to real-world problems, work with datasets, and gain experience in data preprocessing, model building, and evaluation. Furthermore, participate in Kaggle competitions, contribute to open-source projects, or work on some personal side projects. These are effective ways for you to gain hands-on experience and enhance your knowledge.

Look for individuals who have experience and expertise in machine learning, either within your current organization, academic institution, or professional network. Reach out to people with a background in ML and who may be willing to mentor you. Importance of this is often overlooked, but do remember to express gratitude and appreciation for your mentor's time and support.

Many people overlook the importance of a strong GitHub profile. A good profile doesn’t necessarily have to look fancy or boast a large number of followers. Rather, it means contributing regularly to open-source projects and consistently writing quality code. This not only demonstrates your technical skills but also your passion for the work.

Building a strong portfolio is very important if you do not have industry experience in this field. Consider some good projects/datasets which you can find on Kaggle. Refrain from using the most basic projects like flower classification or titanic datasets. Find problem statements which are actually relevant to the industry, company, and try to build an end-to-end solution including deployment instead of just a 'one notebook' solution

Consider joining online communities and forums dedicated to machine learning where you can engage with like-minded individuals, share your experiences, and seek advice. Participating in discussions and collaborating on projects can help you gain confidence and build a support network. Additionally, explore opportunities for interdisciplinary collaboration by seeking out fields where machine learning can make an impact but may not be traditionally associated, such as healthcare or environmental science. By applying machine learning to novel domains, you can broaden your skill set and confidence while making meaningful contributions to diverse fields.

- Utilize resources such as online courses, workshops, and books to improve your technical expertise and confidence. - Volunteer for projects or internships to gain hands-on experience and build confidence in your abilities. - Surround yourself with supportive peers and mentors who can offer encouragement and advice.

Ask yourself: is the issue your lack of assertiveness and confidence, or your lack of knowledge and experience? If you believe that you have the needed knowledge and experience, then you need to work on your confidence. Try giving more presentations, taking a Toastmasters class, etc. The best way to build confidence is to take some risks -- starting small -- and show yourself what you are capable of. If you believe that you lack knowledge and experience, then you need to invest in education to obtain that knowledge and in projects that will increase your experience. That's a bit harder -- and will take longer -- than building confidence. But there's no point in being confident if you don't have the knowledge and experience to back it up.