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Unleash Your Inner Data Scientist: Say Goodbye to Kaggle Nonsense!
23
Feb
Feb
Unlocking the Vitality of Machine Learning: A Full Info to Mentorship Machine finding out has turn into a buzzword inside the tech enterprise, and for good function. It has the potential to revolutionize the best way during which we reside and work, from self-driving automobiles to personalised solutions on streaming suppliers. Nevertheless with the entire hype surrounding machine finding out, it is perhaps overwhelming for newbies to know the place to start out out. That’s the place mentorship is on the market in. Mentorship is a worthwhile system for anyone making an attempt to review a new capability, and machine finding out is not any exception. Having a mentor can current guidance, assist, and real-world experience that will’t be current in textbooks or on-line packages. On this whole info, we’ll uncover the benefits of mentorship in the world of machine finding out and discover ways to uncover and make primarily essentially the most of a mentorship different. The Benefits of Mentorship in Machine Learning 1. Personalised Steering and Help One of the biggest advantages of getting a mentor in machine finding out is the personalized guidance and assist they may current. With a mentor, you might have any person who can reply your questions, current solutions in your work, and provide advice primarily based on their private experiences. This might prevent a big quantity of time and frustration compared with attempting to find out points out by your self. 2. Precise-World Experience Whereas textbooks and on-line packages can current a steady foundation in machine finding out, nothing beats real-world experience. A mentor who has been working inside the space for a while can provide insights and smart knowledge that will’t be current in a textbook. They’ll moreover share their very personal errors and courses realized, serving to you steer clear of widespread pitfalls and pace up your finding out. 3. Networking Alternate options Having a mentor inside the machine finding out enterprise may even open up networking alternate options. They might introduce you to completely different professionals inside the space, current solutions for job alternate options, or even provide to connect you with potential employers. This can be invaluable for setting up your expert group and advancing your career. Discovering a Mentor in Machine Learning Now that now we have established some great benefits of mentorship in machine finding out, the next step is discovering a mentor. Listed under are some solutions to help you get started: 1. Attain Out to Your Group Start by reaching out to your group of associates, family, and colleagues. You under no circumstances know who might have connections inside the machine finding out enterprise or know any person who does. Don’t be afraid to ask for introductions or solutions. 2. Attend Enterprise Events Attending enterprise events, resembling conferences or meetups, is an efficient strategy to meet professionals inside the machine finding out space. Strike up conversations and categorical your curiosity in discovering a mentor. It is potential you will be shocked at how ready people are to help and provide guidance. 3. Benefit from On-line Platforms There are moreover on-line platforms significantly designed for mentorship, such as MentorCruise and MentorNet. These platforms match mentees with mentors based mostly totally on their pursuits and targets. This could possibly be a pleasant alternative for discovering a mentor who’s a good match in your explicit desires. Making the Most of Your Mentorship As quickly as you might have found a mentor, it’s important to reap the benefits of the different. Proper right here are some solutions that may help you get primarily essentially the most out of your mentorship: 1. Set Clear Targets Sooner than starting your mentorship, it’s important to set clear targets for what you want to acquire. This will help info your mentorship and assure that you simply’re every on the related net web page. 2. Be Proactive Don’t wait in your mentor to realize out to you. Take the initiative to schedule widespread check-ins and ask for solutions in your progress. This reveals your mentor that you simply’re devoted and extreme about finding out. 3. Be Open to Options Your mentor is there that may help you improve, so be open to their solutions and choices. Don’t take criticism personally, nevertheless as an alternative use it as a chance to develop and improve. In conclusion, mentorship is a worthwhile system for anyone making an attempt to research machine finding out. It presents personalized guidance, real-world experience, and networking alternate options that will pace up your finding out and career improvement. By following the following advice, yow will uncover and make primarily essentially the most of a mentorship different in the thrilling world of machine finding out. The Vitality of Mentorship in Machine Learning: A Full Info to Unlocking its Potential Machine finding out has turn into a popular time interval inside the tech enterprise, and for good function. Its potential to remodel our lives and work is immense, from self-driving automobiles to personalised solutions on streaming suppliers. Nonetheless, for newbies, the hype surrounding machine finding out is perhaps overwhelming and sophisticated. That’s the place mentorship is on the market in. Mentorship is a worthwhile system for finding out any new capability, and machine finding out is not any exception. A mentor can provide guidance, assist, and real-world experience that may not be current in textbooks or on-line packages. On this whole info, we’re going to uncover some great benefits of mentorship on the earth of machine finding out and the best way to go looking out and make primarily essentially the most of a mentorship different. The Advantages of Mentorship in Machine Learning 1. Personalised Steering and Help Having a mentor in machine finding out presents personalized guidance and assist, which is one amongst its biggest advantages. With a mentor, you might have any person who can reply your questions, current solutions in your work, and provide advice based mostly totally on their private experiences. This might prevent a big quantity of time and frustration compared with attempting to find out points out on your private. 2. Precise-World Experience Whereas textbooks and on-line packages can current a steady foundation in machine finding out, nothing beats real-world experience. A mentor who has been working inside the space for a while can provide insights and smart knowledge that cannot be current in a textbook. They’ll moreover share their private errors and courses realized, serving to you steer clear of widespread pitfalls and pace up your finding out. 3. Networking Alternate options Having a mentor inside the machine finding out enterprise may even open up networking alternate options. They could introduce you to completely different professionals in the sector, current solutions for job alternate options, or even provide to connect you with potential employers. This can be invaluable for setting up your expert group and advancing your career. Discovering a Mentor in Machine Learning Now that we have established the benefits of mentorship in machine finding out, the next step is discovering a mentor. Listed under are some solutions that may help you get started: 1. Attain Out to Your Group Start by reaching out to your group of associates, family, and colleagues. You under no circumstances know who might have connections inside the machine finding out enterprise or know any person who does. Don’t be afraid to ask for introductions or solutions. 2. Attend Enterprise Events Attending enterprise events, such as conferences or meetups, is a good choice to meet professionals in the machine finding out space. Strike up conversations and categorical your curiosity to seek out a mentor. It is potential you will be shocked at how ready people are to help and provide guidance. 3. Benefit from On-line Platforms There are moreover on-line platforms significantly designed for mentorship, resembling MentorCruise and MentorNet. These platforms match mentees with mentors based mostly totally on their pursuits and targets. This might be a super alternative for finding a mentor who’s an environment friendly match in your explicit desires. Making the Most of Your Mentorship Upon getting found a mentor, it is vitally essential make the most of the prospect. Listed under are some solutions that may help you get primarily essentially the most out of your mentorship: 1. Set Clear Targets Sooner than starting your mentorship, it is important to set clear targets for what you want to acquire. This will help info your mentorship and be sure that you simply are every on the equivalent net web page. 2. Be Proactive Do not wait in your mentor to realize out to you. Take the initiative to schedule widespread check-ins and ask for solutions in your progress. This reveals your mentor that you simply simply are devoted and extreme about finding out. 3. Be Open to Options Your mentor is there that may help you improve, so be open to their solutions and choices. Do not take criticism personally, nevertheless as an alternative use it as a chance to develop and improve. In conclusion, mentorship is a worthwhile system for anyone making an attempt to review machine finding out. It presents personalized guidance, real-world experience, and networking alternate options that will pace up your finding out and career improvement. By following the following advice, yow will uncover and reap the benefits of of a mentorship different in the thrilling world of machine finding out.
Speaking as a data scientist in academia, getting your data together is CERTAINLY the hardest part of any report or project. In real life, rarely do we ever enjoy the luxury of clean data being handed to you. Sure sometimes you do! And we're thankful for those moments. But this is decidedly NOT the norm in any sense of the term, so the description of Kaggle being for playtime not prime time is pretty damn true.
thanks, this was enlightening. i guess this is why you chose the name "data JANITOR", because like you just said, sourcing and cleaning the data is the hardest parts of it all?
So what in your opinion is the best way to get intuitive expertise in ML? I mean the state in which vast majority of your coding you don't google or chat but just do it from your head (and you do it right). Probably becoming ML engineer is the only right way to get there but do you have in mind any other way? Would it be self-created, tasted, deployed web app based on ML with focus on quality, some quick models in colab with focus on quantity, maybe some alternatives for Kaggle or instead doing projects it's better to learn theory, read documentations and don't waste time on coding something that I don't fully understand?
Kaggle is more than just a competition platform; it's a comprehensive learning ecosystem for anyone interested in data science and machine learning.
Kaggle competition use an “ensemble” method. The ensemble method is to combine multiple models to improve the performance of a prediction task. The idea is to capitalize on the strengths and minimize the weaknesses of each individual model.
Ensemble methods are often more robust and accurate than individual models, particularly in complex tasks with large datasets.
I'm feeling lost and confused. It seems like data cleansing is the crucial aspect of being a Data Analyst, comprising about 90% of the job. However, you say that modeling data is only around 10% of the role.
To increase my chances of being hired by a company as a data analyst or in machine learning, what specific skills should I focus on? Where should I channel my efforts to enhance my employability?
Any resources (if not kaggle) we should consider investing our time??
This is real, am in a competition where my model is 0.42.. accurate yet am in top 10, when I fine tune for accuracy my ranking drops in kaggle competion
then what to do ?
Ok then where should we practice ?
So where can i find great data scientists to outsource?
Nice insight! Now you owe us for providing better platform for beginner, chief!
Thanks for the video! Can I ask your opinion on whether Kaggle is a good place to practice making good visualizations and well-written code with comments instead of trying to reach that top 1%?
Maybe companies do not make hiring decisions based on Kaggle competitions, but it doesn't mean it is useless. I know a lot of my friends gained incredible amount on knowledge in ML domain participating in these competitions; it helped them crack tough job interviews.
What do you suggest instead?
yeah man but hr managers dont know this so its still great
Hi Mike!
If we can't count on Kaggle, where can we find real life databases?
How useful to work with them and make projects for our portfolio.
Thanks for the information you share, I find it unique. You unmask a lot of people in this business.
I used to write my own machine learning algorithm implementations (in c#) to compete in Kaggle – nowadays you're only allowed to use python notebooks for most of the competitions. So I'm stuck using the same tools as everyone else, which takes the majority of the fun out of it for me. Also, 90% of the top of the board are people copying someone elses notebook and simply changing some random seed. I guess they want bragging rights for being high on the list. For my money, Kaggle used to be waaay better, but it was always extremely artificial and people will always game their entries to do good on the test even if it means doing poorly in a real application.
Why in the world I'm I seeing your videos just now after so many months of wasted time on bullshit certs and courses.? I'm coming for your training Sir. Never come accross anyone like you in the data sphere Like even the terminologies you use like "data roles", "data analyst" etc, there's just so much intention and specificity behind them.The sense of direction you give me is most appreciated. max respect!!
hey i am a student and want to explore and learn machine learning, if not kaggle, where should i start? is machine learning course by andrew ng on coursera good?
thank u so much …..
I'm a little lost, I see jupyter notebooks using pytorch and tensorflow, and yes, XGboost on research papers in bioinforamtics (my field). Is that not considered real engineering?
Dont give a shit what you say, kaggle is good way to put hands into a problem and try techniques and also develop a stable pipeline with different kind of experiments
Unfortunately, I have found you today and have subscribed.
Guess it is only good for familiarity with the concepts
Never did kaggle, bcs was busy fixing data and doubting it.
Thanks, I thought i was crazy, because Kaggle is such a "famous" platform, but not having to do ETL and also seeing models with 100% accuracy win money… wtf? Anything with more than 95% accuracy already makes me sad, because certainly there IS a problem on the model or ETL.
I am studying Data Science. This is the third career. The first one was related to humanities fields, so I did loads of qualitative research in real life. Now that I am facing this new career, and while studying statistics, my obsession is to know where the data comes from, if it is representative, and a long etc. I think you are absolutely right: applying tools is the easiest part. Getting the raw material and organising it properly is the tricky part. BTW… I always wondered how people could get jobs after these short courses. My feeling is that more you know, more you realise how ignorant you are. Just my opinion…
What is you take on Database Administrator role for entry level?
As a web developer can I jump straight to ML Eng ??
In Bachelor in CS we did a lot of Probability theory and statistics so I feel I got the foundational math needed
What do u think ??
Or Data analyst is a must for ML Eng
Do I buy your ML Eng and go with that or with Data analyst ?
This is true, in twitter there was recently a discussion of a Nvidia Engineer who work by evaluating models using their GPUs, he said explicitly, "we get the clean data, we submit to kaggle competitions and wait". Very play time for a ML engineer, but again, he doesn´t solve real world problems.
On my resume, I add Kaggle as a hobby not an actual source of experience. I see Kaggle as a ML sandbox kinda what Qwiklab is for Linux or Cloud learning.
I agree with you about having a ready clean data on Kaggle and building best score is useless but people can learn machine learning application to data. So if I cannot use Kaggle projects as experience then I need to find my own projects I guess
Damn back in the days I saw people with top Kaggle as someone to look up to. As I grow and gained experience, software engineering is about manage entropy, rather than pure programming.
without doubt you are hero for people who really wanna be PROS without stucking with typical nonesence
Stop this man now. He's making too much sense, hehe. You have to be careful. DS bootcamps are coming for you.