How can you be sure you are a good fit for Machine Learning mentorship?

I am motivated to learn machine learning to harness its transformative potential in solving real-world challenges and contribute to sustainable solutions. My goals include innovating with machine learning, conducting research, specializing in multimodal learning and AI ethics, exploring entrepreneurship, and actively contributing to the AI community. I see machine learning as a tool for positive change and strive to stay at the forefront of advancements in this field.

Climate change mitigation is personal to me. Witnessing its effects fueled my determination to contribute. Learning about ML's potential in environmental sustainability motivated me to apply my skills. I'm driven by a deep sense of responsibility and the belief that technology, especially ML, can drive impactful change. The urgency to address climate challenges propels my continuous learning and pursuit of innovative solutions.

Motivation for learning a field is not what you need. You simply need a reason or an idea that could benefit from ML. If you don't have it, it's completely fine too, ML is not an essential skill. Problem solving is. It's a tool that right now is being popularised & widely used due to its benefits. The reason why I'm interested in AI is simply due to 2 reasons. 1. It's a powerful tool to master. I think of it like a Swiss knife, One tool to solve various problems. 2. I like studying it and experimenting with it.

To me, being motivated isn't just about having goals; it's also about the process. I've noticed that folks who enjoy?the process of learning and problem-solving in machine learning frequently outperform others. Happiness in small wins and learning from mistakes are both important. This way of thinking helps people engage with machine learning in a deeper and longer-lasting way, beyond just meeting a goal.

Machine learning opens up a whole new world of exploring solutions previously not possible either due to limitations of computing power ability capture data. Today's world is full of data which can provides us a treasure trove of information and analytics capability. Machine learning in not limited to a few elite group as there are so many opensource resource available to help us use past data to predict future trends, identify issues before it's about to happen and prescribe solutions. This is all very exiting and the use cares are only limited by our imagination. The potential machine learning and artificial intelligence holds that are yet to be unlocked is my motivation to pursue it.

It's important to remember that everyone's path in machine learning is different. Don't look at someone else's middle and compare your starting to it. Your unique experiences, even if they are from different areas, can help solve machine learning problems in useful ways. Embrace what makes you different and use that to your advantage.

You should have some background in math and statistics not only to understand the framework that you will be using but (even more important) to validate your training data and understand the, e.g., correct choice of internal processing in the system

I have a solid understanding of the mathematical and algorithmic fundamentals required for machine learning, what I lack is an overall perspective of this domain. Suppose that concepts and jargon are data of the ML domain, interrelation and mapping of concepts and subjects is information, application of these concepts from various subjects to a standardized problem is knowledge, and applying all these to a real-world problem in an altogether different domain is wisdom. Then I would say I'm between the information and the knowledge phase. And I want reach to the wisdom phase. Given my B.Tech. background in production and industrial engineering I have an urge to solve problems and use conventional and newer tools and ideas to solve them.

One of the most scary things newcomers face in this field is being startled by the math or the ability to code when starting out. Both are not required at at the level that you think it is. Reason being most of the math in ML is elegantly hid behind abstractions of great libraries like ScikitLearn and TensorFlow. And to code you just need to pick up python which is a very beginner friendly language. Its sorta like learning to drive a car. You don't need to know the mechanics of the car to drive it. You only need to understand what make the car move, steer & stop. And as for background, you just need an active internet connection, high school level math understanding with basic programming knowledge(any language) to start out.

My greatest and longest expertise in ML is in building multi-label classifiers in sparse datasets along with the data engineering tasks around daily model retraining, serving and inference. I can analyse performance metrics and move forwards with model quality. My weakness I would say is the field of reinforced learning, Q-Learning and robotics where I only have made theoretical knowledge rather than experience.

Learning comes from industry experts who are building foundation classes and framwworks. cloud based services for model building to deploying in the age of faster delivery makes a difference. Open source with stringent security checks helping big way.

I think it's important to understand that people's ways of learning change over time. Things that work for you now might not work as well in the future. As a data scientist, I think it's important to try out new ways of learning and tools on a regular basis. This not only keeps your learning interesting, but it also fits with the way ML is always changing.

Machine learning is not limited to academic settings, there is a lot of information available today on the web with great examples and hands on how to. There are lots of opensource resources available for the most complicated ML models. I like to learn by doing hands on following examples available and I am not shy to try beyond what the educational materials provide, trying with parameters that are considered crazy just to see it's impact. One of key learnings id dealing with machine learning is sharpen our statistics skills, this will go a long way to help validate the models we build and choose the right features, and models.

Learning style matters, but less than the willingness to be open to your mentor's teaching style. Machine learning mentorships, like other technical mentorships, are a bit different than typical mentorship programs in that they are usually more hands-on. Typically, I will ask my mentees to explore the data set & share their findings with me. Then we talk about the context & I pose questions to help them frame the problem. At that point, they tackle the problem. Later we talk about the approach they took & discuss their findings. I may offer suggestions on ways they could improve their approach &/or or offer how I would have taken on the challenge. Learning style here is less structured - I want to be a guide & encourage them to grow.

I am avid tech enthusiast who is also a hands on learner. For me research and repetition helps the most. I am also super comfortable with reading documentation and resources for research and learning. Progress on the other hand I like to track on a weekly basis. Ideally I like to create new mini-projects on a weekly basis with all the new technologies I have learnt. This let's me gauge how far I have come in the week and accordingly I adjust my pace of learning.

Learning machine learning should not result in memorizing approaches suggested by others but it should include "looking into other fields" like optimization, neural and brain sciences. It is important that you do not memorize results but try to understand what is improved in the different approaches to become an expert and not a "walking library". You need to be ready not only to invest time but also to talk to people from other fields.

I’m notorious for signing myself up for way more than I can realistically handle, avoid this! Once you have the thought of diving into a machine learning mentorship take some time to assess your current schedule. For example, if you currently working a 9-5 reflect on how you feel when you are done your work day, do you feel motivated to tackle machine learning tasks into the evening? How about on the weekend? Would you be willing to devote your down time to learning complex topics? Self reflection and self observation will aid in adequately assessing if you’re a good fit for mentorship.

It's essential to realistically assess how much time and effort you can commit to learning, considering other personal and professional obligations. For instance, if you're working full-time, it might be feasible to dedicate evenings or weekends to learning machine learning concepts. A mentor can help structure a learning plan that fits into your schedule, but this requires honest communication about your time constraints. Balancing your learning with other responsibilities without overextending yourself is key to maintaining consistent progress and avoiding burnout. It’s about finding a sustainable rhythm for your learning journey, ensuring that you can steadily grow your skills without adversely impacting other areas of your life.

Managing your time while learning machine learning is about both how much you learn and how well you learn it. Longer study sessions that are fragmented?can often be less useful than shorter ones that are focused and uninterrupted. Also, make time to rest and think, because these times often lead to new ideas and help you remember what you've learned.

As a field of interest, I don't think of limiting myself by fixing a period. Despite this, I devote 4-5 hours specifically to learning new things, apart from work, and keep myself open as an opportunistic learner throughout the day. At times of stress or frustration, I prefer to keep things away for a while and come back later to ponder. I do this to avoid losing interest or fearing the particular concept because of my inability to understand it at a particular time.

Communicating our ideas should be easy to understand and rich in visuals. I like to communicate my ideas as a well formed report which rather in a story telling style than a long document. I believe the best communication should be able to present our ideas with few words and more visuals without leaving any critical information and should be understandable irrespective of the level of technical capability.

Within the field of machine learning, I've learned that talking to people isn't just about clarity; it's also about collaboration. Sharing your ideas and learning from others can help you understand things a lot better. Join groups and talk with your peers. Also, don't forget how helpful it is to explain challenging ideas to people who aren't experts; it will help you understand them better yourself.

I feel I am an audio-visual communicator. And with respect to machine learning I feel being on the same page is often more important than having parity of knowledge. A lot of times similar ideas have led to creative aha moments which is why I believe clarity and persistence is the most important in communication in most Machine Learning fields.

I communicate ideas succinctly, ensuring clarity. Actively listening, I value diverse perspectives, incorporating advice with an open mind. I collaborate by engaging with learners and experts, participating in discussions, and sharing insights. Feedback is crucial for improvement, and I approach it constructively. My aim is to communicate respectfully, fostering a positive learning environment. Mentors' guidance in enhancing communication is valuable, helping me be more articulate, receptive to feedback, and connected within the learning community.

If you're a person who can relate real life examples to how algorithms work in machine learning then obviously you're the one who can explain things to anyone easily. It's just how effectively you make the audience digest the concept by making them learn step by step ??

From my perspective, having mentors at every stage of your career is beneficial, but the qualities you seek in them will vary. As a junior, it is highly advantageous to have a mentor who instills excitement in exploring different subjects, algorithms, or projects. Such mentors won't merely provide answers but will guide you along the path you need to follow. As you progress to a mid or senior position, you'll benefit from someone who challenges you, not only in a different area but also with a diverse set of skills. Being an exceptional coder is valuable, but lacking communication skills may hinder your progress.

I believe having a good mentor in your learning journey makes your transition from beginner to intermediate and followed by advanced learner quite smooth. Sometimes it is hard to learn just by hitting the books, you need a different perspective or explanation by someone who has gone through the same issue that you are facing. Also, often mentors as an experienced person in the field would be able to give some interesting real life examples and experiences that would make the learning quite enjoyable.

1. Solid ML Knowledge Base ?? 2. Hands-On Experience ??? 3. Clear Communication ??? 4. Patience and Empathy ?? 5. Commitment to Learning ?? 6. Teaching Passion ?? 7. Problem-Solving Acumen ?? 8. Ethical Mindset ?? Reflecting on these qualities helps me gauge my readiness and effectiveness as an ML mentor. It's a journey of continuous growth and learning, not just for the mentee but for the mentor as well.

In my opinion, there is a couple of topics that people lack when they want to apply machine learning in their projects. The first one, you need to have a question you want to answer. You may have exploration phases, visualization of data, etc.. but having a goal and a question to answer is key to success. And the second, it is important that the data scientist understands the data, the field, the goal, have background on the nature of the data, etc... if we just give data and ask for magic... it does not work... the data scientist need to be involved and understand the background and nature of the problem.

In my journey, I've found immense value in integrating knowledge from various fields. For instance, principles of psychology and sociology can provide deeper insights into consumer behavior, enhancing the predictive accuracy of ML models in market analysis. Personal Story: A pivotal moment in my career was leading a team that developed an ML-based solution for optimizing supply chain logistics. This project not only improved operational efficiencies but also significantly reduced environmental impact. It was a testament to how ML can be a force for good.

Aus meiner Sicht ist es sehr wichtig sich nicht nur rein auf das Maschinelle Lernen und die Tools dazu zu fokussieren. Wichtig ist es die Dom?ne in welcher eine L?sung gefordert wird ebenfalls zu verstehen. Bevor der UseCase und die Daten nicht verstanden sind ist das Maschinelle Lernen nicht zielgerichtet genug. Am Ende sollten man immer den Nutzer im Hinterkopf haben. Dazu sollte man ihn m?glichst gut verstehen, auch dessen Ausgangsbedingungen, wie muss z.B. das Ergebnis aufbereitet werden. Tut man dies nicht kann das schnell zu Nichtakzeptanz auf Seiten der Nutzer führen.

One valuable lesson gained from my mentor is the cultivation of critical thinking skills. When you encounter someone who empowers you to think independently, it's essential to cherish and retain that connection.