Te interesa la ciencia de datos. ¿Cómo puede obtener más información sobre el aprendizaje automático?

You can dive into Machine Learning in various ways: (1) Free Online Courses: Stanford CS lectures, Coursera etc. offer tons of free introductory and advanced machine learning courses. You can explore courses based on your interests! (2) Hands-on Practice: Knowledge is power, but practice is key! Use free datasets and compute resources from Kaggle and Google to experiment and build your own projects. (3) Join the Community: Don't go solo! You can join online communities or attend in-person meetups to network with passionate learners and experts ready who can answer your questions and share insights. Remember that learning ML is a continuous journey. Explore resources, dive deep, build your portfolio & you can launch your career in ML!

To deepen your understanding of machine learning, you can enroll in online courses like Coursera's Machine Learning by Andrew Ng 📚, participate in Kaggle competitions to apply concepts practically 🏆, and collaborate with peers on open-source ML projects 🤝.

You have various options to learn machine learning, depending on what you want to achieve, your background, and what you prefer. Platforms like Coursera, Udemy, and edX offer online courses covering the basics of machine learning, common algorithms, and how to use them in real-life situations. However, it's important not to solely rely on these courses. Taking notes with pen and paper can help you grasp concepts more deeply, giving you a better understanding of algorithms and helping you choose the right one for your business needs.

Now a days everyone is learning machine learning but how you can be differentiate from others is you have to learn more and more algorithms. With most important need to learn about maths behind algorithm. Then only you can increase the accuracy from other

When I started, learning from fast.ai and practicing on Kaggle or working on my own projects is what helped me. Just pick a project where you need to write your own Dataloader (Dataset class in torch) and create a custom model architecture. You will gain clearly in a lot of things.

To delve into machine learning, begin with online courses from platforms like Coursera or edX, and study foundational texts and papers to understand underlying theories. Engage in hands-on projects using datasets from Kaggle to apply what you've learned. Join forums like Stack Overflow and Reddit’s machine learning community to exchange ideas. Attend relevant workshops and conferences for the latest insights. Gain practical experience by initiating your own projects or contributing to open-source efforts. This multifaceted approach will solidify your machine learning knowledge and skills.