What are effective ways to learn Machine Learning as a self-employed individual?

?? Growing in machine learning is a self-made adventure! I'd recommend setting clear goals to focus your learning journey. Follow a structured curriculum from reliable sources, enriching your foundation. Hands-on projects are the playgrounds where you apply your knowledge - dive into them! Engage with the vibrant ML community for insights, support, and inspiration. Remember, ML is an ever-evolving field, so keep your learning continuous and up-to-date. Most importantly, enjoy the process. Embrace the challenges and celebrate the milestones. Let's embark on this exciting learning odyssey with curiosity and passion! ????

Selecting a goal involves a strategic fusion of personal passion, market demand, and skill alignment. Start by reflecting on your own interests and strengths, considering areas within ML that captivate your curiosity. Simultaneously, evaluate the current and emerging trends in the industry, identifying opportunities where your skills can meet market demands. Assess your existing skill set and identify the areas where you can leverage your strengths while embracing opportunities for growth. A self-employed individual should aim for a goal that not only aligns with their intrinsic motivation but also positions them strategically within the evolving landscape of ML applications.

Yeah honestly, I wouldn't try following any course. Get your hands dirty! Start your own project and google when you don't understand things. As you fail and solve your problems, you'll get a visceral understanding of ML. Running through some TensorFlow collab notebooks can't hurt either.

Embarking on a self-learning journey in Machine Learning can be both rewarding and enjoyable. Start by setting clear goals and a structured roadmap, breaking down the vast field into manageable topics. Engage with diverse learning resources such as online courses, tutorials, and books. Practical application is key, so work on real-world projects to reinforce theoretical knowledge. Join ML communities to connect with like-minded individuals, share experiences, and seek guidance. Embrace challenges, stay curious, and celebrate small victories to maintain enthusiasm throughout the learning process.

Choosing a specific goal in machine learning that aligns with your interests and skills is indeed a crucial first step. It provides direction, helps in focusing your learning efforts, and allows for a more structured approach. Whether it's building a recommender system, a chatbot, a computer vision application, or a natural language processing tool, having a clear goal not only motivates your learning but also aids in finding relevant resources and mentors to support the journey.

Following a structured curriculum is a crucial step in learning machine learning. With a abundance of online courses, books, blogs, podcasts, and videos available, it's essential to choose a curriculum that aligns with your learning style, background, and goals. Beginners may benefit from foundational courses like "Machine Learning" by Andrew Ng, while those more advanced might prefer in-depth explorations such as "Deep Learning" by Ian Goodfellow or "Natural Language Processing" by Dan Jurafsky. Tailoring your learning path to your level and interests ensures a more effective and engaging learning experience.

Have a conversation with an LLM and use it to help you structure your own personalized curriculum taking into account your learning objectives. Read technical blogs from Spotify Engineering and other companies that apply ML in the real world.

There are may good MOOCs where you can learn machine learning by following a structured curriculum. I suggest DeepLearning.AI's courses for an excellent overview. Be aware however, that the days where hobbyists and small business owners build their own models are probably over. With the arrival of transformers for natural language (and soon for visual artifacts and timeseries data) hobbyists can no longer build and train their own models. It requires enormous resources that is unaffordable for the average Jo. I suggest you find a course that helps you understand transfer learning and retrieval augmented generation to get the most out of generative AI in the future.

Following a free curriculum like the Stanford CS229: Machine Learning Course on youtube is quite effective if you are selfthought, good at time management and able to motivate yourself. You can also take on free version of machine learning courses on coursera but you will not get access to the tutors and assigment corrections. Sometimes paying for something add an extra layer of motivation!

Use online platforms like Coursera, edX, or Khan Academy to follow structured machine learning courses. Popular choices include Andrew Ng’s Machine Learning course and the fast.ai courses for deep learning.

Embarking on hands-on machine learning projects that resonate with your interests can provide a stimulating and enriching learning experience. Consider venturing into image classification tasks, where you can personally collect, label, and refine a dataset of images. This hands-on approach allows you to delve into the intricacies of image classification, gaining valuable insights into model training, evaluation, and optimization.

I find working with case studies, and previously modeled problems to test and understand the similarities and differences between the performances of other conventional modeling approaches versus machine learning models to be the best way to not only learn more about machine learning, but also to build intuition towards what types of models and ML architectures work better with different problems. Such comparative frameworks also help develop an understanding of the algorithmic and computational complexity and efficiency of ML approaches, which is particularly challenging to ascertain at the prototyping stage.

Engaging in real-world projects is the third critical step in the machine learning learning journey. Platforms like Kaggle, GitHub, or Google Colab offer a wealth of project ideas and datasets. Creating your projects, tailored to your data, interests, or client needs, is also a valuable approach. Projects not only showcase your skills and build your portfolio but also boost confidence. They serve as tangible evidence of your capabilities and credibility, crucial if you're aiming to establish yourself as a proficient machine learning practitioner. This hands-on experience provides a practical perspective, reinforcing theoretical knowledge with practical application.

Learning machine learning is a dynamic process that thrives on active engagement. Building projects is crucial because it aligns theory with application, reinforcing your understanding. Importantly, failure is an inherent part of the learning journey. Through facing challenges and overcoming setbacks in the project-building process, you gain invaluable insights and resilience. Many practitioners find that the knowledge gained from hands-on project building surpasses that acquired through formal courses and theoretical study.

There are so many amazing online resources to learn Machine Learning nowadays. Udemy, OpenCV University, Carnegie Mellon University online are my top picks. Learning anything new requires a base knowledge to develop confidence and skill before you can dive deep. I would suggest for anyone starting their ML journey to check out the sites I mentioned and see what beginner machine learning courses pique your interest the most and take those. You're going to need a large supply of motivation, fascination and interest in your particular machine learning area so choose wisely. Once you know where you're heading, don't stop until you become an expert. There is a lot to learn so take it slowly. Slow and steady wins the race everytime.

Some Machine Learning topics can have a complex theoretical background, which can be a little intimidating if it is your first contact with the subject. Joining a community of learners and enthusiasts can be very beneficial to overcome these difficulties. Engaging with forums, attending meetups, participating in online discussions, and following the latest research can provide valuable insights, support, and inspiration. Also, asking questions and explaining topics that you are already confident about can boost your understanding about Machine Learning and make your learning processes much easier.

Engage with the machine learning community to learn from others, seek help, and stay motivated. Participate in forums like Stack Overflow, Reddit’s r/MachineLearning, or attend local meetups and conferences to network with professionals in the field.

Joining a community can certainly be useful. Some communities, such as INFORMS, can be a great platform for workshopping things that you're working on or learning about. Mentorship is also another option. Machine Learning concepts can cover a lot of breadth and depth, and this vastness may be confusing especially in the beginning - a mentor may be able to point you in the right direction to get started, or guide you in creating the best learning path for your interests.

It is advisable to follow prominent AI researchers on LinkedIn, who often share insightful and informative papers related to machine learning.

Machine learning is a rapidly evolving field. Stay updated with the latest research papers, attend webinars, and explore new frameworks or tools. Continuous learning is crucial for staying relevant and improving your skills over time.

One of the most effective ways to actually learn, is by doing, avoid the heavy copy paste situations, for example when you dont remember the name of a method it's generally more effective to use '__dir__()' dunder method of the base classe to find it out than to google it.

Continuous learning is very important in the dynamic landscape of machine learning. Embracing curiosity and staying receptive to new ideas, methods, and tools is crucial for enhancing capabilities and staying relevant. Seeking feedback from peers, mentors, or clients serves as a valuable source of improvement, turning mistakes into learning opportunities. The ability to adapt and evolve not only fuels personal growth but also positions you as a resilient and competitive self-employed machine learning professional. In a field that evolves rapidly, the commitment to perpetual learning is a cornerstone for success.

Machine learning is an ever evolving field so you can become overwhelm by the advancement quite fast. But remember, as long as you work on challenging problem, the constant learning will come in hands with delivering great solution! paperswithcode.com is also a great ressource that you can look at to see and try for yourself the latest research!

Machine Learning is one of the most trending fields of our time. It is always evolving, which sometimes can be difficult to deal with. As a Machine Learning student you will have the ability to exercise your knowledge with practical projects along the way. This is a great source of encouragement to go on and overcome the difficulties because you can actually see the knowledge you gathered becoming tangible. The more advanced you are in your curriculum of studies, the more intricate and complex your projects should become, allowing you to have a good grasp of your progress and also build a portfolio along the way, showcasing your skills and accomplishments

As in all disciplines, the best approach is to learn by doing! Clearly experimenting in this area may not be easy without the proper resources, but after obtaining basic knowledge through courses and online resources (of which there are plenty), one can start developing something small even on platforms such as Colab... and when you are good enough, let's go for Kaggle Competitions!

One piece of advice I would like to have received when I started my Machine Learning journey is: start from the beginning. Nowadays we see many complex Deep Learning architectures and powerful models that draws a lot of attention. In most cases, these models and architectures make use of several advanced and complex topics that can be overwhelming for someone new to the field. This can be quite discouraging and frustrating. If you start from less complex topics, building up your knowledge, when you are ready to study these complex subjects you will be much more prepared, and your learning will be much easier and more effective.

Learn machine learning through online courses, hands-on projects, and participation in communities like Kaggle. Stay updated with specialized courses, open-source contributions, and networking events. Create a portfolio to showcase your skills.

The most important part of the AI field is in the application to real world problems that can't be solved by other means. The problems best suited for AI are complex and difficult to manage. The hardware infrastructure alone that is required to process and handle the shear magnitude of data is cost prohibitive for many and yet a prerequisite to this field. We are still in the early days of AI and the next 5-10 years will be revolutionary for many fields. Visual arts will be dominated by AI in 2-3 years. Everything is life is a data point, and it's up to you to decide what data points you're going to spend your time doing. Have fun, stay safe and to God be the Glory!

There are two fundamental dimensions of learning ML/AI - theory-based and applications-based. This isn't unique to ML/AI by any means but as relevant as any other field of study. Time spent learning theory will take away from time spent learning how to apply that theory. As your education progresses, consider which focus is likely to support your career goals. Are you targeting a research-based role? If that's the case, a heavier focus on theory is warranted. Are you targeting an engineering role? A more applications-based approach, focused on building real-world projects, is likely to offer the greatest benefit. In either case, more knowledge of either will benefit you. Such a decision is more about specialization than anything.