What do you do if you want to kickstart your Machine Learning career with practical experience?

Start by building a solid foundation in machine learning fundamentals, including concepts like supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics. Online courses, textbooks, and tutorials are excellent resources for learning these basics

To kickstart your Machine Learning career with practical experience, focus on projects. Start with small, manageable tasks like Kaggle competitions or personal projects. Collaborate with peers, contribute to open-source projects, and seek internships or freelance opportunities. Continuous learning and hands-on practice are key.

To kickstart your Machine Learning career practically: Master Foundations: Understand ML concepts deeply for confident application. Core Understanding Over Projects: Prioritize core concepts over creating numerous projects. Learn CPU/GPU Basics: Grasp CPU/GPU architecture to advocate for optimal server configurations. Business Context: Comprehend business problems for aligned ML solutions. Practice EDA and Statistics: Engage in EDA and basic statistics practice for data insight extraction. Enhance Communication: Develop clear and persuasive communication skills for effective stakeholder engagement.

Get your hands dirty! Just get started, it doesn't need to be perfect. I personally used kaggle heavily in the beginning of my ML journey and it was very helpful. It's a great platform where you can compete with other people and you get to see how others approach the problem as well!

To kickstart your Machine Learning career with practical experience, prioritize working on projects. Choose projects that align with your interests and career goals, and actively seek out datasets and resources to work with. Apply machine learning techniques to solve real-world problems, and document your process and results. Whether it's through personal projects, internships, or freelance work, hands-on experience with projects will help you build practical skills, demonstrate your abilities to potential employers, and establish a strong foundation for your Machine Learning career.

Hands-on experience is crucial for learning machine learning. Start by working on small projects to apply the concepts you've learned. Kaggle competitions, personal projects, or contributions to open-source projects are great ways to gain practical experience and showcase your skills.

Develop practical ML skills through real-world projects using diverse datasets from platforms like Kaggle or UCI ML Repository. Experiment with classification, regression, and NLP tasks to hone problem-solving and model-building skills. Working on projects boosts your portfolio and expertise in ML.

Share your ML work for feedback and exposure on platforms like GitHub or LinkedIn. Engage in online communities such as Reddit or Stack Overflow for learning and collaboration. Sharing fosters skill development and enhances visibility in the ML community.

To kickstart your Machine Learning career with practical experience, learning from others is invaluable. Seek out mentors, join online communities, and engage in collaborative projects. Networking with professionals in the field can provide insights, guidance, and even potential job opportunities. Participating in hackathons, workshops, and open-source projects allows you to apply theoretical knowledge to real-world problems while learning from peers with diverse perspectives. Additionally, don't hesitate to ask questions, share your own insights, and actively contribute to discussions. By embracing a collaborative approach, you'll accelerate your learning curve and build a strong foundation for a successful career in Machine Learning.

Network with ML professionals through events like meetups and online groups for opportunities and support. Join platforms like Slack or attend workshops to connect and exchange ideas. Building connections fosters referrals and a strong professional network, propelling your ML career forward.

If you're a student... GET AN INTERNSHIP. Like immediately. I like to think of it like a hack to learning a skill, you get the mentorship, practical experience, and most of the time the theoretical info. Research opportunities at universities are also heavily slept and are cheat codes to gain practical-ish experience. Ask a professor at your school if they can point you in the right direction for undergraduate research. I would suggest you have a list of topics you are interested in. Resources to find opportunities: - Brilliant Black Minds - Rewriting the Code - ColorStack -Handshake I found plenty of opportunities by following people in my desired industry and frequently looking for opportunities on LinkedIn.