What do you do if you want to excel in Machine Learning but lack a strong foundation in mathematics?

You don't need to be a math major to do machine learning. But you do need a basic understanding of vector geometry and trigonometry and at least enough calculus to understand what optimization using gradient descent means and how it works. Some linear algebra is useful too, since a lot of machine learning involves matrices. All of this is basic university-level math. But you may have forgotten what you learned in those classes, or perhaps you never took them. You can find great material online for free, whether on YouTube or Khan Academy. Take the time to brush up on these fundamentals. Trust me, you'll need them to be successful in machine learning.

You need the basics of : - statistics - linear algebra - probability - calculas You can learn from YouTube or online courses like MIT OCW

Like learning a language, you can make progress in Machine Learning just by doing. To "excel" in language, however, you need to develop a strong foundation. In ML one of the critical foundations is math. So, continually challenge yourself to manually calculate ever more complex topics: Dot products, Matrix multiplication, Gradient descent, T-test, Enjoy!

Avoid overly relying on libraries like Scikit-Learn during the initial stages of your machine learning journey. When starting out, you typically encounter smaller problems and less complex models, making it the ideal opportunity to write code from scratch. This approach fosters a deeper understanding of the underlying processes and encourages you to revisit or learn mathematical concepts. It's an adaptive method for mastering the mathematics relevant to machine learning. As you witness the practical utility of these concepts, you'll be more motivated to learn them, leading to more enjoyable and productive active learning experiences.

I'd utilize beginner-friendly programming libraries like scikit-learn or TensorFlow and visualization tools to implement algorithms and gain insights into data patterns. Additionally, I'd engage in online courses and tutorials for practical demonstrations, accelerating my learning journey in Machine Learning.

In addition to useful libraries, there is currently a wealth of resources dedicated to visualizing concepts in machine learning, which can greatly aid in developing an intuition for ML. Explore the GitHub pages of renowned contributors, where you'll find an abundance of these helpful and engaging tools.

Leverage tools like TensorFlow, PyTorch, or scikit-learn with high-level APIs to streamline model development without deep mathematical understanding. Utilize pre-built algorithms and libraries for specific tasks, leverage autoML platforms for automated model selection, and focus on practical applications where understanding the business problem is paramount. Additionally, invest time in learning fundamental mathematical concepts gradually to enhance long-term proficiency.

For starters, I'd use beginner-friendly programming libraries like scikit-learn or TensorFlow along with visualization tools to implement algorithms and understand data patterns. Then, I'd leverage higher-level APIs in tools like TensorFlow or PyTorch to streamline model development, even without deep math knowledge. I'd also explore pre-built algorithms and autoML platforms for tasks that don't require heavy math. Alongside, I'd gradually learn fundamental math concepts to boost long-term proficiency.

You'd need to learn/know how to use at least one programming languages. Python is one very popular programming tool and it has tonnes of libraries that implement the mathematical concepts used in machine learning

The most important thing is to know the domain where you want to apply ML. Identify what are the challenges in the domain that can be addressed by ML. Then, learn about the typical ML methods and techniques that can help with these challenges, with an emphasis on the ideas and the logic behind the algorithms rather than the mathematical principles. Make a mind map of problems and solutions and keep expanding it as you learn more. This mind map helps to identify the optimal solutions for specific problems.

I've often observed students proficient in coding who, upon delving into machine learning, swiftly grasp the basic concepts and begin coding immediately. Their rapid progress might lead them to swiftly simulate complex and sizable models within a few months. However, their haste often sacrifices depth of understanding and creativity. Without a solid grasp of the underlying concepts, they inadvertently construct a lofty tower on a frail foundation, which risks collapse.

This one resonates with me as I prioritize conceptual understanding over complex math. It emphasizes grasping the intuition behind ML algorithms, which aligns with my learning style.

Understanding why a certain algorithm is necessary to achieve one's objective reflect how much one has understand the problem at hand. One the algorithm is determined it will be just a matter of using the right tools to breath life into it.

I would actively engage with the community to leverage collective knowledge and support. This involves participating in online forums, discussion groups, and social media communities dedicated to Machine Learning. By asking questions, sharing experiences, and collaborating with peers, I can gain insights, learn from others' expertise, and receive guidance on overcoming mathematical challenges. Additionally, I would attend meetups, workshops, and conferences where I can interact with industry professionals and academic experts, fostering connections and expanding my network. Through active engagement in the community, I can access diverse perspectives, resources, and mentorship opportunities to enhance my learning and progress in ML.

One thing I've found particularly helpful, especially in keeping up with the latest ML developments, is forming an online group with other learners. We assign each member a specific topic to become familiar with and then teach it to the rest of the group. This approach allows each of us to deepen our understanding by teaching others, while also enabling everyone to learn about a variety of topics through concise and engaging lectures. Over time, this method ensures that we remain consistently updated.

Yes, read about articles, papers and conversations from people within the ML discipline. Keep up with new ideas and discoveries.

I would focus on practicing beginner-friendly projects aligned with my interests. Leveraging online tutorials and guided project resources, I'd gain hands-on experience in implementing machine learning algorithms, gradually building confidence and proficiency. Through consistent practice and iterative learning, I aim to enhance my skills and expand my capabilities in Machine Learning.

One useful approach I've discovered is setting up small projects as milestones along your learning journey. For instance, if you're diving into Recurrent Neural Networks (RNNs), consider creating a mini music generation project trained on your favorite playlists. This not only serves as a significant motivator to complete the task but also transforms your learning process into active engagement. As you tackle the project, your mind actively seeks solutions, facilitating a deeper understanding of the concepts involved.

No matter how much your hear or read about anything, practicing breaches understanding gap. There is always a gap and the only way to bridge that gap it to try to test your understanding. Start small and keep moving and sooner or later you'd start feeling like and authority in this area.

Keep doing more projects so that you can able to learn more and more concepts and at same time you may face a some issue or unknown error which could help us to rectify and study it. More pratice is good for gaining skill in better way

My belief is that you define a big project for yourself and divide it into details and different phases. Set deadlines for each phase and try to follow each phase until 100% realization. Finally, the joy of doing a big project is trust. It will give you an incredible gift

I prioritize continuous learning through self-study, staying updated with industry trends, engaging in practical exercises, and seeking mentorship. Through these efforts, I aim to gradually improve my skills and proficiency in Machine Learning, despite initial mathematical limitations.

focus on incremental learning. Start with beginner-friendly resources like Andrew Ng's "Machine Learning" course on Coursera or "Python for Data Science and Machine Learning Bootcamp" by Jose Portilla on Udemy. Gradually delve into deeper concepts using textbooks like "Introduction to Statistical Learning" and "Pattern Recognition and Machine Learning" by Bishop. Leverage online platforms like Khan Academy and Brilliant for targeted math refreshers. Participate in coding challenges on platforms like Kaggle and LeetCode to apply theoretical knowledge practically. Seek mentorship from experienced practitioners and join supportive communities like Stack Overflow and Reddit's r/MachineLearning.

Continously learning each day is good for a data professional in order to upskill and upgrade to further position in career. With Continous learning your value in the market would grow in best way.

Want to ace Machine Learning, but math gives you chills? Don't sweat it! Here's your launchpad: 1. Focus on Code & Intuition: Start with coding (Python) and understanding core concepts. Develop a feel for how algorithms work. 2. Visualize the Math: Use online resources and libraries like scikit-learn that provide visual representations of mathematical concepts. 3. Practice Makes Progress: Dive into practical applications and online tutorials. Hands-on experience is key! BUT, with this remember, math is the language of ML. While you can start without it, for long-term mastery, tackling core math (linear algebra, calculus) is inevitable. Think of it as leveling up your ML superpowers! All the best!

I actually purposely wrote my entire masters dissertation on Computational Psychology and Machine Perception without any serious maths. This worked against me with my professors who wanted to see more concrete things. I was lucky in the sense that I demonstrated the research with software which was able to do a lot of the heavy lifting for me. However, I think the larger part of my success with this was playing to the strengths of my research. As it was on perception and psychology the mathematics didn't have a critical part. It's literally asking what makes a difference of 0.01 in a specific neuron mean? Practically nothing. You need to understand the basics, but should also play to your strengths within a field that values it ??.

If lacking a strong math foundation in Machine Learning, focus on building skills incrementally through online courses, tutorials, and practical projects. Emphasize understanding key concepts like linear algebra, calculus, and probability/statistics, seek guidance from mentors or peers, and leverage libraries and frameworks with high-level abstractions to initially mitigate mathematical complexities while gradually deepening your understanding through consistent practice and self-study.

The main idea of understanding maths is to visualise the algorithms. Their mechanisms so that they can be tuned based on the problem. Mathematics helps in vusualizing in a more structured manner. Our own cognitive visualization is great but using maths we can give some structure and using this structure we can craft different ML algorithms based on the problem.

If you’re lacking the foundational mathematics necessary to understand machine learning from a theoretical perspective, find a trustworthy source/textbook on its applications. An understanding about how and when to use certain machine learning algorithms has equal value to understanding the underlying mathematics.