Here's how you can establish your personal brand in Machine Learning as a beginner.

Setting clear goals is essential to direct learning efforts in the field of Machine Learning. I always use the following key points for good goal setting: 1. Choose one or two subfields to focus on; 2. Definition of Short-term Objectives, such as acquiring basic knowledge and completing simple projects; 3. Defining Long-term Goals: Become an expert, publish articles and develop innovative applications; 4. Use of SMART goals: Specific, Measurable, Attainable, Relevant and Time-bound; 5. Creation of an action plan, how to network and plan practical projects; 6. Assessment and adjustment of progress; 7. Seek feedback from more experienced people about my projects.

When it comes to diving into any technology and domain. Its very important to note the following points(as per me):- 1. Why do you want to do it? 2. What are the pre requisites?- very important to start any technology cause ultimately its the basics which will be the game changer. 3. Do have small and clear goals. 4. Work both theoretically as well as practically. 5. Be mentally prepared that its gonna be difficult.

Setting a specific future goal in a rapidly evolving field like machine learning can be difficult. Currently, field like computer vision (CV) is highly crowded and the emergence of models like GPT has led many to shift towards natural language processing (NLP). This trend may continue to change in the future. Therefore, in journey to build a personal brand in ML, one can focus on short-term goals to master various domains in ML . In the long term, one can aim to serve as a bridge between technical and non-technical audiences.

I started by selecting a focus area in ML - computer vision. Then I dove into practical work: I built a basic image classifier, joined a CV-focused hackathon, and contributed to an open-source computer vision library. These experiences taught me more than just studying theory. I realized that concrete skills in my chosen domain matter more than self-marketing. Employers and peers were more interested in my specific CV projects than how I branded my general ML knowledge. Now, I emphasize practical work in computer vision. My focused portfolio demonstrates my abilities better than any broad personal brand could. My advice: Choose a specific ML domain, then focus on doing real work in that area.

Clear Goals - End to End Roadmap : It is vital to have complete roadmap what to share at what stage to share. Having a clear goals will help you to stay focus and output will be result oriented. It is also vital to have AI/ML domain understanding along with Business domain understanding. And these need to align in your content creation to build personal brand from my opinion. Share precise and clear content.

Before diving into machine learning, it's crucial to define your personal brand's goals. Do you want to be known for expertise in a specific area, like natural language processing or computer vision? Or are you aiming to bridge technical and non-technical audiences by simplifying ML concepts? Clearly articulating these goals will guide your learning path, projects, and content creation. This focus ensures your efforts align with your brand objectives, helping you build a coherent narrative and effectively showcase your unique value in the machine learning field.

Decide what impact you want to have in the ML community and who you aim to help. This focus will shape your learning, projects, and collaborations, aligning everything with your long-term vision and making your efforts more meaningful.

Before diving into machine learning, set clear, achievable goals for your personal brand. Define your expertise, whether it's in a specific ML domain like NLP or computer vision, or as a bridge between technical and non-technical audiences. This focus will guide your learning path, projects, and content creation, aligning with your brand objectives.

Establishing a personal brand in Machine Learning as a beginner requires strategic steps: 📖Skill Development: Master core concepts like statistics, algorithms, and programming languages (Python, R). 📖Continuous Learning: Stay updated with industry trends, new algorithms, and tools. 🚀 Networking: Engage in ML communities, attend conferences, and connect with professionals. 💻Content Creation: Write blogs, contribute to open-source, or share insights on platforms like Medium or LinkedIn. 💻Portfolio Curation: Build a portfolio showcasing diverse ML projects and their impact. 🌟By consistently showcasing expertise, networking, and contributing to the community, a strong personal brand in Machine Learning can be cultivated effectively.

Define Goals: Set career objectives and focus on specific skills. Learn Publicly: Share your learning journey and updates on social media. Engage with the Community: Join discussions and connect with professionals. Showcase Projects: Post detailed work on platforms like GitHub or blogs. Continuous Learning: Stay updated with courses, webinars, and research. Network Effectively: Build industry connections and attend events. Additional Tips: Find a mentor and be consistent and patient.

Establishing your personal brand in Machine Learning as a beginner is best achieved through learning publicly. As an AI/ML Consultant, I've seen the power of sharing your learning journey. Documenting your progress on platforms like GitHub, LinkedIn, or personal blogs not only showcases your skills but also demonstrates commitment and transparency. Share code snippets, project updates, and reflections on challenges and breakthroughs. Engaging with the community through discussions, feedback, and contributions fosters connections and builds credibility. This public learning approach accelerates your growth, highlights your passion, and positions you as a proactive learner in the ML field.

Personal branding is built through knowledge sharing; there's no better way to do this than sharing knowledge publicly. Today, there are numerous social networks and platforms where this is possible. I recommend starting as soon as possible, as this ensures a competitive advantage.

"What" to learn and "How" to learn is not a fixed methodology. It varies with respect to time. So, the best way is to learn by discussing. Share your ideas, questions, and the things you have learned on platforms like LinkedIn, technical discussion forums, and more. There will always be people to help, guide, and appreciate you for that. Try to add all your work to GitHub and keep it up-to-date. Participate in AI/ML hackathons; even if you don't win, you'll gain more ideas and can grow your network. The main and important thing is: Always appreciate each other, even for small achievements, and keep the positivity around you.

Sharing articulate articles on grasped concepts and unique experimental insights sparks dynamic discussions among peers, fostering innovative approaches to tackling real-world problems. It's a pathway to collaborative exploration and problem-solving, enriching our collective understanding and driving forward meaningful advancements.

I started documenting my ML journey online, focusing on my computer vision projects. At first, it felt awkward sharing my learning process, but it turned out to be incredibly valuable: 1. Accountability: Posting regular updates kept me motivated and consistent. 2. Networking: I connected with other CV enthusiasts and even found a mentor through my blog. 3. Portfolio building: My blog and GitHub became a living showcase of my progress. 4. Unexpected opportunities: A company reached out after seeing my project writeups. However, I learned to balance sharing with actual work. It's easy to get caught up in the "appearance" of learning. I make sure most of my time is still spent on hands-on coding and project work.

Documenting your journey can take various forms, such as blogging about the machine learning concepts you're mastering, sharing your projects on GitHub, or posting updates on social media platforms. By sharing your progress and insights, you not only demonstrate your growing expertise but also show your commitment to continuous learning and improvement. This approach creates opportunities to connect with like-minded individuals and potential mentors who can provide valuable guidance and support. Public learning makes your progress tangible, both to yourself and others, reinforcing your personal brand every step of the way. Embrace this journey of learning out loud, and watch your personal brand grow in authenticity and influence.

Learning publicly is a highly effective strategy for establishing your personal brand in Machine Learning. This involves sharing your learning journey, including the resources you are using, the projects you are working on, the challenges you encounter, and the milestones you achieve. Platforms like LinkedIn, Twitter, GitHub, and personal blogs are excellent for this purpose. By documenting and sharing your experiences, you not only demonstrate your commitment and progress but also position yourself as a proactive and engaged learner. This transparency can attract feedback, mentorship, and collaboration opportunities from more experienced professionals in the field, accelerating your growth and enhancing your visibility in the ML community.

Through public learning, I've discovered 4 key benefits that have accelerated my Machine Learning journey - 1. Improve your Credibility and Trust 2. Enhanced Visibility and Network 3. Bring Opportunities and Recognition 4. Boost your Learning Process Bonus Tip: Consistent public learning demonstrates your dedication and passion for the field, making you a reliable source of information and people start perceiving you as an Expert.

One of the best ways to build your personal brand is to learn in public. Share your journey by blogging about the machine learning concepts you're learning, sharing projects on GitHub, or posting updates on social media. This not only showcases your developing expertise but also demonstrates your dedication to continuous learning and growth. It's a chance to connect with peers and potential mentors who can offer advice and encouragement. By making your learning journey visible, you not only strengthen your personal brand but also inspire others and reinforce your own progress.

Engaging with the machine learning community is crucial. I participate in online forums, attend meetups and conferences, and join ML-related groups. This helps me learn from others, share my insights, and get feedback on my work. By being active and helpful, I build relationships and establish myself as a collaborative and knowledgeable member of the community. Remember, your personal brand grows not just through what you know, but also through the networks you build and the value you provide to others.

Although I don't have enough experience regarding Community engagement, but I have been in touch with my friend who has been very proactive in building communities and engage with like minded individuals. Based on his experience I can vouch that Community engagement can be vital in creating personal brand. The biggest benefit according to me is, it helps you in establishing solid connections which are based on common interests and focused interactions. Not only that it help you improve your reputation and recognition in the domain. Be very mindful to focus on quality over quantity and be authentic and helpful.

I joined online ML forums and local meetups. Shared my projects, asked questions, and helped others when I could. This led to: 1. Learning from experienced practitioners 2. Getting feedback on my work 3. Building a network of peers and mentors 4. Discovering job opportunities 5. Staying updated on industry trends Community engagement improved my skills and visibility. It's not just about self-promotion, but genuine interaction and mutual support. Key takeaway: Actively participate in ML communities. It enhances your learning and naturally builds your reputation.

Engaging with the Machine Learning community is essential for building your personal brand. Participate in online forums, discussion groups, and social media platforms where ML topics are discussed. Join relevant communities on LinkedIn, Reddit, or specialized platforms like Kaggle and GitHub. Contribute to discussions, ask questions, share insights, and offer help to others. Attending and presenting at ML conferences, webinars, and meetups can also enhance your visibility and credibility. Active engagement shows that you are passionate about ML and committed to continuous learning and collaboration, helping you build a strong network of peers, mentors, and potential collaborators.

Get involved in ML communities by joining forums, attending meetups, and participating in online groups. Share your insights, learn from others, and offer assistance to build relationships and establish credibility.

Machine Learning is one of the top tech skills. Lots and lots of events, conferences and ML-related groups are being established every day. Finding a community and engaging with them is quite an easy task. But finding the community that has specifications that you are particularly interested in growing the brand is crucial. Networking with peers through multiple platforms will open tons of opportunities and gives you insightful ideas for your personal brand growth.

Learn from others, share insights, and get feedback. Being active and helpful builds relationships and establishes you as a knowledgeable, collaborative member. Your personal brand grows through your knowledge and the networks you build and the value you provide.

Engaging with the machine learning community has significantly enriched my journey. Early in my career, I joined forums like Reddit's r/MachineLearning and attended local meetups. These platforms were invaluable for learning from peers, sharing my projects, and getting constructive feedback. I remember presenting my work on LLMs at a conference, which not only boosted my confidence but also led to meaningful connections with industry experts. By actively participating and offering help where I could, I built a reputation as a collaborative and knowledgeable professional. Building your personal brand involves not just showcasing your expertise, but also fostering relationships and contributing to the community.

Engaging with the community has been invaluable in my journey to establish a personal brand in machine learning. I have been part of various communities where I interact with experienced professionals who create amazing blogs and projects. From these interactions, I learn a lot and seek guidance for my projects, which helps me grow both technically and professionally. This active participation not only enhances my skills but also helps me build a strong reputation within the community and among my peers. Through these connections, I am able to stay updated, share my knowledge, and contribute meaningfully to the field.

Establishing your personal brand in Machine Learning (ML) as a beginner involves engaging with the community. Participate actively in online forums like Stack Overflow and GitHub, where you can contribute to discussions and showcase your projects. Join ML communities on platforms like LinkedIn and attend meetups, webinars, and conferences to network with professionals. Share your knowledge through blog posts, tutorials, or social media to demonstrate expertise and build credibility. Engaging with the ML community not only enhances visibility but also fosters learning and collaboration opportunities crucial for personal brand development.

Although I haven't had enough experience to provide advice on building a personal brand in machine learning, I have worked on projects with someone who has. Through these alliances, I've found that displaying your machine learning initiatives can effectively establish your brand. You can demonstrate your abilities and inventiveness by writing about the steps involved in solving problems, the difficulties encountered, and the solutions did work out. Sites such as GitHub are great for showing off your code and offering comprehensive documentation. This openness displays your technical proficiency, problem-solving methodology, and readiness to absorb mistakes. These intimate tales and encounters connect with others and set your brand apart.

Share everything you work on publicly, even if they seem like small or insignificant details. Utilize all available platforms and resources at your disposal. Be transparent and authentic in your sharing.

Sharing your projects is a key aspect of establishing your personal brand in Machine Learning. Develop and showcase projects that highlight your skills and knowledge. These projects can be anything from data analysis and visualization tasks to building complex ML models and applications. Use platforms like GitHub to share your code and detailed documentation. Write blog posts or create videos to explain your projects, the problems they solve, the methodologies used, and the results achieved. Sharing projects not only demonstrates your practical abilities but also provides tangible evidence of your expertise and creativity, making your personal brand more compelling and trustworthy.

I've noticed quite a few times that folks pushing code on GitHub sometimes don't encrypt sensitive data and credentials. This can lead to negative publicity for your brand, as data security is one of the most important factors in today's world. Additionally, it's crucial to make sure that your claims are tested thoroughly. So, while it's important to push code and keep everything transparent, just be cautious about your claims and ensure sensitive information is properly secured.

Machine learning projects are a great way to showcase your skills and creativity. Share the details of your projects by writing about the problems you tackled, the challenges you encountered, and the solutions you came up with. Use platforms like GitHub to share your code and include thorough documentation. Being open about your process not only demonstrates your technical abilities but also shows your problem-solving approach and your willingness to learn from mistakes. These personal stories and experiences make your work relatable and help distinguish your brand.

Sharing ML projects is essential to highlight skills and attract more views to your profile. Describe the problem-solving process concisely, highlighting challenges and innovative solutions. Use platforms like GitHub to showcase code and document each step of the project in an engaging way. By being transparent about the difficulties faced and the lessons learned, not only demonstrate technical skills, but also the capacity for professional growth. These practices not only strengthen trust in ML but also help build a distinctive personal brand in the field.

Here's my approach to sharing projects: 1. Projects: - Clear README with project overview - Explanation of data preprocessing steps - Model architecture and training process - Results and performance metrics 2. Blogs: - Problem statement and motivation - Challenges encountered and how I overcame them - Key learnings and unexpected insights 3. I'm honest about setbacks and failures. It shows I'm learning and improving. 4. I include visualizations and demos when possible. It makes projects more accessible. 5. I link my project repos in my resume and LinkedIn profile. This approach has helped potential employers and collaborators understand my skills.

Collaborate on projects, articles, or research papers with other professionals. These collaborations can introduce to new insights and add credibility to machine learning expertise.

Your machine learning projects showcase your skills and creativity. Share them comprehensively by detailing your problem-solving process, the challenges faced, and the solutions implemented. Use platforms like GitHub to display your code and provide thorough documentation. This transparency highlights your technical abilities, problem-solving approach, and willingness to learn from mistakes. Personal stories and experiences resonate with others and distinguish your brand. By sharing your journey, you not only demonstrate expertise but also inspire and connect with the machine learning community.

The world of ML is like JS frameworks when it comes to innovation and change; what you learned last month is at best outdated if not obsolete altogether. This is mostly a joke until you look back on how much the field of ML has changed in the last few years. Follow the latest papers in the field that you are interested in and don't forget to follow top researchers who have a voice in the industry.

Here's my approach to continuous learning in ML: 1. Follow key ML researchers and companies on social media for latest updates 2. Attend webinars and virtual conferences for industry insights 3. Read ML papers regularly: - Focus on my chosen domain - Start with abstracts and conclusions - Fully digest 1-2 papers per week - Try to implement key ideas from impactful papers 4. Discuss new concepts in study groups or online forums 5. Revisit and update old projects with new knowledge This ongoing learning keeps my skills sharp and provides fresh content for my blog and community discussions. It's crucial for staying relevant in the fast-paced ML field.

Continuous learning is vital in the rapidly evolving field of Machine Learning. Stay updated with the latest research, tools, and technologies by following reputable sources like academic journals, industry blogs, and online courses. Enroll in advanced courses and certifications to deepen your knowledge and expand your skill set. Regularly practice and experiment with new techniques and algorithms. Participate in hackathons and coding competitions to challenge yourself and learn from others. By committing to lifelong learning, you show that you are dedicated to staying at the forefront of the field, which strengthens your personal brand and enhances your professional value.

In the ever-evolving field of ML, continuous learning is essential. I made it a habit to stay updated with the latest research, tools, and techniques. Enrolling in online courses, reading books, and following thought leaders kept my knowledge fresh and relevant. This commitment to lifelong learning fuels your content creation and discussions, keeping your personal brand dynamic and engaging.

Continuous learning is crucial in the rapidly evolving field of machine learning. Stay current with the latest research, tools, and techniques by taking online courses, reading relevant books, and following industry thought leaders. Consistently expanding your knowledge and skills demonstrates dedication to your profession. This commitment to ongoing education will enhance your content creation, enrich community discussions, and improve project outcomes, ensuring your personal brand remains relevant and engaging.

Machine learning is ever-evolving, so commit to continuous learning to maintain a relevant personal brand. Stay updated with the latest research, tools, and techniques. Enroll in online courses, read books, and follow thought leaders in the field. By consistently expanding your knowledge base and skill set, you demonstrate dedication to your craft. This ongoing education fuels your content creation, community discussions, and project work, keeping your personal brand fresh and engaging. Show your commitment to growth and adaptability, and your personal brand will thrive in the dynamic machine learning landscape.

The world of machine learning is rapidly growing industry and we can think that I'm 100% done with this domain. There will be always several methods to solve one problem and we may not be knowing all. So, always be adaptive and ready to accept change. you can make use of "code with paper" form or there are several LinkedIn influencers and newsletters and communities which share the latest ML related works happening around the world. Create habit of reading Research papers. initially it will be little bit hard for beginners but believe me this skill will take you to master level. Always be a good leaner...

Continuous learning has been crucial in sustaining my relevance in the machine learning field. I regularly follow the latest research, attend webinars, and take advanced courses to stay current. For example, I recently completed a course on transformer architectures to deepen my understanding of LLMs. Reading influential books and following thought leaders on platforms like Twitter keeps me informed about new trends and techniques. This commitment to ongoing education not only enhances my expertise but also provides fresh material for my blog and discussions, ensuring my personal brand remains vibrant and up-to-date. Continuous learning is key to demonstrating your dedication and staying ahead in the ML landscape.

The tech world moves fast. Hence to stay informed on the latest trends, tools, and methodologies. Regularly participating in webinars, online courses, or tech podcasts can keep our knowledge fresh. This continuous learning not only bolsters our expertise but also ensures we remain relevant and forward-thinking.

Connect with professionals who share your goals and interests, and actively engage with their content. Offer help, seek collaborations, and foster meaningful relationships. Personal branding in machine learning goes beyond showcasing your skills; it's about the connections you make and the positive impact you have on others. A well-cultivated network can unlock opportunities that enhance your brand and accelerate your career growth.

Building a personal brand in ML can be daunting, but effective networking can accelerate success. Key strategies: LinkedIn: Join ML groups, share projects, and connect with experts. GitHub: Share code, contribute to open-source projects, and collaborate. Meetups & Conferences: Participate actively and follow up with personalized messages. Communities: Be active on Stack Overflow, Reddit, and join project collaborations. Mentorship: Identify mentors, and reach out with specific requests. Showcase Work: Create a portfolio on GitHub or a personal site, and share knowledge through blogs or tutorials. Stay Updated: Follow industry news and engage in continuous learning. Strategic networking builds a strong ML brand and unlocks opportunities.

Building your personal brand in machine learning also hinges on smart networking. Connect with professionals who share your goals and interests, and engage meaningfully with their content. Offer assistance, seek collaborative opportunities, and nurture genuine relationships. Remember, personal branding isn't just about showcasing skills; it's about the connections you cultivate and the positive influence you create. A strategic network can pave the way for new opportunities that elevate your brand and accelerate your professional growth in the field.

Networking smartly is crucial for building a strong personal brand in Machine Learning. Connect with professionals, researchers, and influencers in the ML community through platforms like LinkedIn, Twitter, and professional associations. Attend industry conferences, seminars, and workshops to meet people face-to-face and build meaningful relationships. Seek out mentors who can provide guidance and support. Engage in meaningful conversations, share your insights, and listen to others. Building a diverse and supportive network not only provides opportunities for collaboration and learning but also helps amplify your personal brand by associating you with respected figures in the field.

Smart networking isn't just about expanding your connections. Make connections with professionals who align with your goals and interests and not just with anybody randomly. You never know your insights or work might impress someone and you might grasp your next internship/job.

Smart networking is pivotal in establishing a brand. Connect with professionals who align with your interests and goals. Engaging with their content, offering help, and seeking collaborations adds depth to these connections. Networking isn't just about increasing your contacts; it's about building meaningful relationships that open doors to new opportunities and accelerate growth.

Build your brand in machine learning by smart networking. Connect with professionals who share your goals and interests. Engage with their content, offer assistance, and look for collaboration opportunities. Personal branding isn't just about showcasing skills; it's about relationships and impact. A strategic network can lead to opportunities that boost your brand and accelerate your career in ML.

Building a solid network is essential to strengthening your personal brand in Machine Learning. By actively participating in discussion groups focused on natural language processing (NLP), such as specialized online forums or in-person events, you can collaborate with experts and professionals in the field. These environments provide opportunities to discuss trends, share experiences, and get direct feedback on your projects and ideas. These interactions not only expand your technical knowledge, but also increase your visibility in the ML community, helping you build relationships that could result in future partnerships or job opportunities.

Build a professional online presence on platforms like LinkedIn, GitHub, and Medium. Share your projects, write about your learning journey, and connect with other professionals in the field.

As a beginner, it is essential to remember that there's no one AI model that fits all!! What works well for one domain may not for another. It is important to understand the domain and dataset nuances before actually building an AI/ML model.

Certification - Collaboration - Research paper - Patent Get certified: There are plenty of certification Provided by Giants like Google, Meta, AWS, Deep learning. Al etc. Choose the correct one and do it at correct time. Collaboration: Try to collaborate with people who have working on the same idea your thinking to work, communicate with them through Email, LinkedIn, etc. and try to work with them that will help you to grow. Research paper: Try to find out the area you want to focus and apply ML skill to solve real world problem. write your own research paper & start your research journey. Patent: On your research journey if you think you invented something unique, try to get patent on that. It will really help your career.

Certifications are a good way to keep yourself updated in the domain. Excelling in ML requires extensive learning and time. Seek opportunities like free Externships that provide real time learning and project experience where you can interact with companies. Build your portfolio by starting with basic projects and gradually moving to more complex ones. Many sites offer free learning opportunities and Job Simulation experiences for students, helping you gain the necessary skills. Collaborating with people on projects is a great way to improve your communication skills too.

Recently, there has been a significant focus on MLOps, which benefits the industry. However, distinguishing between MLOps and DevOps can be challenging for newcomers. Although the fact of the matter stands that both MLOps and DevOps are focused on the operation side of things, there are key differences when ML is added to the equation. Here are a few: 1. Your models and datasets are also considered artifacts, just like the code and application binaries that you work with in DevOps. 2. Your lifecycle management has to consider continuous training as well. 3. Monitoring has to include model performance, data drift, and prediction accuracy. 4. A good automation pipeline has plans for data preprocessing, model training, and evaluation.

In addition to the strategies mentioned, consider maintaining a professional online presence by creating a personal website or portfolio that showcases your skills, projects, and achievements. Regularly update your profiles on LinkedIn, GitHub, and other relevant platforms. Use professional language and visuals to present yourself effectively. Seek feedback from peers and mentors to continuously improve your brand. Finally, be authentic and true to yourself; your personal brand should reflect your unique strengths, values, and passions. By combining these elements, you can establish a strong and authentic personal brand in Machine Learning that stands out and drives your career forward.

8. Get Certified: Obtain certifications from reputable organizations or online platforms like Coursera, edX, or Udacity to validate your skills and knowledge. 9. Seek Mentorship: Find mentors who can guide you, provide feedback, and help you navigate your career path in machine learning. 10. Showcase Soft Skills: Highlight your communication, teamwork, and problem-solving abilities, as these are crucial in collaborating with others and effectively presenting your

There are so many platforms these days that helps you to understand ML basics. Go for books if you are more comfortable as it does help while revising but understanding the concepts via video helps you gain insights too.

In a nutshell, if you want to establish yourself as an ML expert, you would need to be thorough in the ML concepts not only theoretical but also practical. Other than this, you have to become a good reader because any new technology such as library, framework, deep learning network, tools etc. comes with documentation and research paper only in the beginning. You should be able to understand the complexity and the depth of the same.

In addition to the strategies mentioned, another critical aspect of building your personal brand in machine learning is to seek out and embrace feedback. Early in my career, I actively sought feedback on my projects and presentations. One memorable instance was when I presented a novel approach to text summarization at a local meetup. The constructive criticism I received not only improved my work but also taught me the value of diverse perspectives.

Focus on a specific area within technology where you possess deep knowledge and passion. This could be anything from machine learning, cybersecurity, cloud computing, to web development practices. A narrow focus allows us to dive deeper and offer more insightful commentary than if we spread ourselves too thin.