Gain 4,000 LinkedIn Followers in Just 6 Months as a Machine Learning Writer — My Simple Methods
LinkedIn connections aren’t just about the quantity; it’s also about the quality and how to make meaningful connections that capture people’s interest.
Engaging on social media, especially professional platforms like LinkedIn, can yield valuable rewards, even if they aren’t immediately visible. Over time, you will realize its importance in your career. Today I want to share how I built connections on LinkedIn from scratch to 4,000 followers (as of September 22, 2023).
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1. About Me
I started my journey writing about Machine Learning on Medium || Github || Kaggle || Linkedin in March 2023. Writing started as an easy way to collect and archive knowledge for future reference. At the time, I only had about 200 connections on LinkedIn, most of which were not in my field of expertise. To my surprise, my first article received a positive response from readers, which motivated me to write more. It’s been about 6 months since I started writing and I now have over 4000 followers on LinkedIn who are interested in my content. 4,000 may not seem like a big number to some people, but I think what I write really matters to 4,000 followers.
2. The Secret to Make It Happen
2.1. I write about what people care
As someone who works a lot with data, I understand how to make data actionable. I pay close attention to the analytics behind each post, observing and comparing which themes resonate with my audience. Some of the posts I’ve written have gotten nearly 3,000 impressions and others just over 10. These numbers provided valuable insight into audience interest.
When I first wrote the article, I had no idea what kind of topics people would like. I was worried that my knowledge wasn’t “professional” enough for people to like it. That was false. Times have changed and you are younger than me. Your ideas from your internship are different from mine from my time. Believe me, you can start writing and sharing, too.
“Your experience even as a junior is something unique to us.”
When choosing a topic to write about, I have several criteria in mind:
“Informative, Fun and Practical”
If your topic meets all 3 of these criteria, we are sure that your post will attract a lot of people. I would like to discuss this case study in detail in another article to avoid this one being too long.
Here are some tips to make your content more interesting:
#1. The audience likes illustrations. Appear as a narrator. You have 3 seconds to capture the reader’s attention. So, would you prefer images or text?
#2. The audience does not like to read. The audience is busy. They get distracted by too much information on the platform. Always keep your messages short and informative.
#3. The audience doesn’t like theory. In my field of machine learning, I often work with models. Like, “What are A, B, and C?” It doesn’t give them any joy. Instead, I focus on practical projects and step-by-step tutorials.
👉 Here, I’ve got some practical projects for you to check out:
- Credit Card Fraud Detection: A Hands-On Project
- A Hands-On RFM Analysis: Discover Customer Segmentation Insights with Python
- Enhancing Customer Churn Prediction with Continuous Experiment Tracking
2.2. Not only do I share what I know, but I also focus on what’s not in the book.
Learning ML from scratch made me realize that the journey is more than just learning what is written in a textbook or online course. It’s navigating uncharted territory that no one seems to discuss, but it’s an essential aspect of being successful in this field.
#1. Nobody told me how ML projects are run on large teams.
In the world of machine learning, working on a project within a large team can be very different than working alone. This includes understanding the nuances of collaboration, communication, and project management within the context of ML.
These are practical aspects that are rarely covered in traditional teaching materials. I believe that by sharing my experiences managing and contributing to ML projects in a team environment, my audience can shed light on important aspects of this field.
#2. Nobody taught me how to approach new technology and make it successful in front of clients.
The adoption and implementation of new technologies go beyond algorithms and models, especially in customer-facing scenarios. It’s about the art of persuasion, building trust, and effectively communicating the value of technology.
Understanding how to bridge the gap between jargon and customer-friendly language and manage expectations is a skill set I must cultivate through trial and error. Sharing this knowledge will help others navigate this complex aspect of the machine-learning environment.
#3. No one encouraged me to use my knowledge to find other sources of income.
Machine learning experts typically have a wealth of knowledge beyond their daily work. They have unique skills and knowledge that can be monetized in a variety of ways, including consulting, freelancing, or creating their own products.
However, understanding how to turn this experience into an additional source of income is rarely addressed in formal education. I have personally considered these methods and can provide guidance on how others can do the same.
👉 I’ve shared my experience of earning $4,000 from freelance work right here. Feel free to take a look:
There will be more and more “Nobody taught me” moments in the future that I want to experience and share with my audience for they know how reality is.
2.3. I don’t miss any opportunity to connect with my audience.
I have a bit of an obsession, you could call it OCD. I absolutely hate the idea of leaving emails and messages unread. I really value the comments and messages I receive through my posts.
It is important for me to know my audience personally, express my gratitude for their support, and listen to their feedback for my continuous improvement. I want to make sure no one in the audience feels ignored when they approach.
Connecting with my audience is a top priority as it helps me better understand their needs and interests and also strengthens the sense of community around the content I create. If you have any questions, would like to share your opinion, or just want to say hi, I’m here and happy to help. Your feedback and interactions are extremely valuable to me and I appreciate all the messages and feedback I receive. Feel free to contact me. We’ll continue this conversation.
Conclusion
My journey as a Machine Learning writer to getting 4,000 followers on LinkedIn in just 6 months has been a rewarding adventure. Sharing my simple methods and knowledge has allowed me to connect with like-minded people passionate about Machine Learning.
Building a strong LinkedIn presence requires dedication to providing valuable content. Interacting with your audience, sharing real-life experiences, and responding to comments and messages are important steps toward success.
Remember, it’s not just the numbers that matter, but the connections you make along the way. If you continue to share your knowledge and experiences while growing your LinkedIn following, you will see your network expand and flourish.
Keep it simple, keep it real, and watch your influence in the Machine Learning community continue to grow.
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