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What do you do if your network isn't helping you advance your career in Machine Learning?

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Networking is often seen as a key factor for advancing your career in machine learning, but what if your network is not helping you achieve your goals? Maybe you are not getting the referrals, opportunities, or feedback that you need to grow and improve your skills. Maybe you feel stuck in your current role or company and want to explore new challenges and domains. Maybe you are looking for mentors, collaborators, or peers who share your passion and vision for machine learning. In this article, we will explore some strategies to overcome these challenges and build a more effective and supportive network for your machine learning career.

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1 Assess your network

The first step is to assess your current network and identify its strengths and weaknesses. Who are the people you interact with regularly, and how do they contribute to your learning and development? Are they supportive, honest, and helpful, or are they indifferent, competitive, or discouraging? Do they have the skills, experience, and connections that you need or want to learn from? Do they expose you to new ideas, perspectives, and opportunities, or do they keep you in your comfort zone? You can use tools like LinkedIn, Twitter, or Meetup to map out your network and see how diverse and relevant it is for your career goals.

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Arunodhayan Sampath Kumar

Research Associate @MihubX and Ph.D. student @Chemnitz University of Technology | Kaggle expert | Freelancer | AI consultant
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Identify Your Regular Interactions: Make a list of the people you interact with regularly in both professional and personal settings. This could include colleagues, friends, family members, mentors, industry peers, and online connections. Evaluate Contributions to Learning and Development: Consider how each person in your network contributes to your learning and development. Are they supportive of your goals? Do they offer valuable feedback and guidance? Are they knowledgeable about topics relevant to your interests? Assess Supportiveness and Honesty: Reflect on the level of support and honesty within your network. Are your connections encouraging and uplifting, or do they tend to discourage or criticize your efforts?

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Bheema Shanker Neyigapula

Application Developer @IBM | M.Tech(CS) @JNTUH '23
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If your network isn't propelling your Machine Learning career forward, consider proactive steps. Firstly, assess your current connections—look for peers, mentors, and industry professionals actively involved in ML. Engage with online communities, attend conferences, and participate in workshops to expand your network. Seek informational interviews to gain insights and build rapport. Additionally, contribute to open-source projects or start your own, showcasing your skills and attracting like-minded professionals. Lastly, prioritize quality over quantity in your connections, focusing on those who offer valuable support and opportunities for growth.

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2 Expand your network

The next step is to expand your network and reach out to new people who can add value to your career. You can do this by attending events, webinars, workshops, or hackathons related to machine learning, and engaging with the speakers, organizers, and participants. You can also join online communities, forums, or groups where you can share your work, ask questions, give feedback, and learn from others. You can also follow, comment, or message influential machine learning experts, researchers, or practitioners who inspire you or have similar interests. You can also create your own content, such as blogs, podcasts, or videos, where you showcase your projects, insights, or opinions on machine learning topics.

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Malvika Singh

Technical Product Management| Senior Software Engineer at Veritas| Software Product Management at UW| Ex-Amazon Web Services (AWS)| 5+ years experience in SaaS| AI Product Management| MS, Data Analytics, Carnegie Mellon
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Joining courses and certifications is also a good way to expand your network especially if these are in-person. The instructors are many times from the industry so they get to know about your capability and can also help make introductions for you in the ML field. You can also request them for recommendations and referrals if they are willing to provide at the end of the course.

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3 Nurture your network

The final step is to nurture your network and maintain meaningful relationships with the people you connect with. You can do this by following up, expressing gratitude, offering help, asking for advice, or sharing resources. You can also set up regular check-ins, calls, or meetings with your network contacts, where you can discuss your progress, challenges, or goals. You can also seek or provide mentorship, collaboration, or referrals with your network contacts, where you can exchange skills, knowledge, or opportunities. You can also celebrate, acknowledge, or endorse your network contacts for their achievements, contributions, or skills.

Networking is not a one-time activity, but a continuous process of building and cultivating relationships that can help you advance your career in machine learning. By assessing, expanding, and nurturing your network, you can create a more supportive and beneficial environment for your learning and growth.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if your network isn't helping you advance your career in Machine Learning?

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2

3

4

1 Assess your network

2 Expand your network

3 Nurture your network

4 Here’s what else to consider

Machine Learning

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What do you do if your network isn't helping you advance your career in Machine Learning?

1

2

3

4

1 Assess your network

2 Expand your network

3 Nurture your network

4 Here’s what else to consider

Machine Learning

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