Thanks for watching everyone! For more videos on projects, check out these: The projects that got me a job: ruclips.net/video/imMPnCHvbkY/видео.html The projects that can get you a job: ruclips.net/video/yukdXV9LR48/видео.html My Favorite Free Learning Resources: ruclips.net/video/Ip50cXvpWY4/видео.html&ab_channel=KenJee 365 Data Science - Paid Courses ( 57% Annual Discount): 365datascience.pxf.io/P0jbBY
Hi, I hope you are doing great. Just wanted to know if it is worth putting projects, that are not end to end, on resume (especially when you have no end to end project and are desperately searching for a job due to financial issues at home)?
Before delving into machine learning I recommend aspiring data scientists hone data cleaning and exploratory data analysis skills. Data familiarization and cleaning being foundational skills. There are smallish datasets that are appropriate for beginners and which can be visually scanned to confirm the findings reported. For example, the automobile fuel efficiency dataset available from UCI contains 'marked' missing values for some numeric data, data entry mistakes primarily in the make and/or model; not every make and model combination is present more than one year, and two fuel types are present. The data source makes available the original dataset as well as a subset still requiring further cleaning, along with a codebook describing the data and each variable. Many instructors tell you to begin by plotting the data but this approach has two problems: you do not know if there are any missing values or other incorrect values which can lead to plots which are erroneous or simply cannot be rendered; and you have not spent sufficient time understanding the raw data and the types of questions it might be able to answer.
I too have recently started to learn the basics of data science and honest;y kaggle is one of my favorite sites to find interesting datasets to work with. I took bio as my major in high school and had dropped maths in my last year so the concepts of statistics and probability and linear algebra got me scared at first but after I spent hours completing courses in those subjects (tons of pdfs and youtube channels and online courses) I feel pretty confident in my effort and knowledge. And I cant wait to dig deeper in this fascinating field of data science!
I think the point you make around 6:30 is a really good one in general for all forms of science and engineering. It's really about practice at the end of the day. We're not born math geniuses, we learn math through repetition and problem-solving. Same thing applies to data science and programming. The more you practice it, the better you get and little by little the overwhelming problems seem a little less... overwhelming :)
As a self learner, I'd say your vids are really helpful. First off, there's something calming about your voice. Secondly, the way you guide regarding the projects, helps me set a clear path rather than rush into things and panick. Keep up the God's work your are doing. Thanks!
"Experiential learning is the best way to learn and go through Data Science", very truly said because that helps us to evaluate where we lack and hence one is able to extend their domain of knowledge. Time to put the pedal to the metal!
Thanks Ken I'm a spanish computer professional. I'm trying to introduce in ML world to change my career and your videos have help me to center my studies.
This is literally the first video I'm looking on data science, and I was a little bit scared of beggining it, but you gave me a lot of confidence when giving these resources and tips! Thank you m8
Thanks Ken, once I have finished learning the fundamentals (midway through online course and Kaggle courses), will get these done, your videos are very helpful!
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Finally we have someone on internet who is really putting you on the right path of learning data science and ML, thanks a tonne! I was completely lost and frustated, I will recommend this channel to my friends and colleagues.
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
I really want to thank you as I already have done first 2 projects during this lockdown. I realized that everything is possible to learn. You are very good person for sharing the invaluable knowledge.
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
I am just starting out my journey in the field of data science and I find your videos to be really informative and at the same time motivating too. It was so kind of you to share your personal experience, thoughts and knowledge about what you know to the world. It really shows how a person's ability lies in their will. So, I really thank you and wish for great life ahead. 😊😊
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
One thing I appreciate about your videos is how you tell us about the challenges we will face as beginners but give us the assurance of overcoming these challenges. Thanks for this video. It means a lot to me.
@@KenJee_ds I’m glad I recieve an early notification from your like and reply. I live in Ghana West Africa and I have knowledge in Python language and I’m now studying data science using books and RUclips videos. Is there a chance I can gain an internship online in the near future while living here in Ghana? I’d love to hear your reply sir ❤️
Hi Ken, I've been watching your videos and really have enjoyed them. I am currently doing an internship in a startup that's developing insect farms (insects for human consumption, mostly crickets). I'm majoring in Industrial Engineering, started my internship in more of a production stand point, but due to the pandemic I've redefined the project for a data driven improvement. In the last couple of months I've helped the company with its data collection and aggregation methodologies and tools. That has led me to be really interested in deepening my knowledge into data science. I'm currently working on the Coursera Data Specialization Courses and will be applying the tips that you've shared in your channel. Keep the good work. Pura Vida!
That sounds like an awesome internship! I would be interested in trying the product. I would love to hear more about it! I am really glad the videos have been helpful to you.
I started studying Data Science in April, I'm definitely going to be working on these projects. Thanks for this channel it is awesome! I just found it...please continue to make quality content :D
Through watching your video, I realized that my school DS project, which was to compare error rates of different classification algorithms, won't count as much as the topics you mentioned... I should really work more on new projects. As a recent undergraduate, I want to start with Titanic classification project. And House price regression project sounds fun too! Thanks for the recommendations!
From last one month I am learning data science but I was little bit confused that where should I apply this knowledge but after watching your video now I can do some projects in data science Thank you.....!😊
Thanks for the tips, expecially the part were you mentioned that you have passed the fear and it is a natural process of learning, because i am having that feeling now, there are so many things to learn.
You're the man, not only highlighting great courses to use and the general way to go about growing in the field but also the words of encouragement for a newbie like me have been extremely helpful. Thank you!!!
Watching in early 2022. I really like this breakdown of process and how to think about solving problems. I'm a musician, producer and recording engineer and a lot of these....learn-by-doing and divergent thought processes remind me a lot of how to get a good mix or composing. Really excited on this journey
This is a great help, I am a bachelors student and I really want to be able to give value in future. I’ve been wondering how to start learning by experience and this video encouraged my search. Thanks.
I thumbed up and watched the video all the way through, why? because you also listed the things out in the description. Most people want to clickbait you into watching the full video so they dont list the content in the description, usually we have to go to the comments for someone to list them up. So thank you its appreciated.
I recently started learning data science and started on this kaggle project of House hold prices. I was trying to establish relationship between the time at which houses are sold to the sale price of houses
Hi Ken. I totally relate with everything you’ve said. I’m currently in grad school and it’s a struggle grasping the concepts and being expected to apply these concepts towards successfully completing the program
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Good luck with your channel. I'm trying to learn data science by myself and Ive been told I'm a quick learner but then I know that I lose interest or motivation even more quickly and have been at this for quite some time without much progress. I think I should go with the projects and learn alongside, hope it works.
I agree! I think going with projects that you are excited about is the best approach to stay motivated! I also made this video that may help with motivation: ruclips.net/video/akbU9KOo_Qc/видео.html
Nice! Having joined Kaggle recently, and due to one of your recommendations (thanks and kuddos by the way :D ) ... I feel that I am now ready to start these kind of projects. Since I did followed successfully the two first courses of Frank kane, it seems like a natural progression for me.
@@KenJee_ds I saw that you had a video about it and will definitely check it out ;) That being said, I'd like to blunder with it a little ... and blunder like making mistakes in the safety of my home ... that kind that come with valuable lessons and everything, after which I'll check your video to compare ;) Also, it will probably gave me a hint as to where my weaknesses are so I can plan my studying accordingly. Anyways, thanks for the channel! As far as I can see, it's full of useful stuff for a beginner like me.
The iris dataset is ideally suited for regression, classification, and clustering projects for beginners. Working with a single dataset and learning which types of machine learning algorithms are possible and appropriate can reduce the time before you can tackle resume-worthy projects. Another useful skills is the ability to compare approaches (regression vs. classification vs. clustering) so you are ready to make recommendations to your manager or client or even your team about why a certain approach or algorithm is better-suited to the task at hand.
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Here's a course you need, it's paid but it's worth it. khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Outstanding video ...love the way you break it down to the bare essentials , as to how we should approach the development of this new set of skills... :-)
I must add that your teaching Algorithm is 95% accurate..... I would have written 100% but that will make your model ( your teaching ) over fit...you know what I mean...
Good stuff! Thank you for sharing. I am taking online courses and your videos give me helpful insights. I also like the short length of your videos which is very efficient. Thanks!
@@icvsmiglani8790 The biggest two things I recommend are 1) building your portfolio out more 2) networking to get a employee referral. This increases your chances by more than 5x! I actually have a course with 365 data science on how to break into the field if you're interested, I go into quite a lot more depth there. I also have a few interviews on my channel with tina huang and data leap where we talk about these topics in depth. I recommend checking those out!
I am a physicist, and I need to learn data, science, and machine learning for experimental physics, material science, 2D material Identification (their quantum properties, and so on). as It seems, I must learn it if I wish to survive in my knowledge domain.
Thanks for this video Ken. I have a sports analytics problem for you. When the 3 point shot was first introduced in the NBA, I said start launching them. Traditionalists said, oh no, too risky, too many will ruin the game. 20+ years later 3 point shots are an integral part of any teams' strategy. OK, my question is regarding the 2 point conversion in football. My belief is that the 2 point conversion should be tried every time. Once a team gets used to it they will develop special teams just for the conversion. Plus, once fully developed, these plays could be used for any regular down inside the 5 yard line. The amount of dedicated plays could be unlimited - 2 QB's on the field, 5 running backs, non-traditional formations, etc. Not to mention how exciting the game would become. Do you have any supporting evidence that, over time, going for the 2 point conversion 100% of the time would pay off? I guess part of your problem is that you cannot measure what hasn't been tried.
To be honest, football is not my area of expertise, but I am a firm believer that coaches go for two far less than they should. There are however, some situations where I think going for the extra point will always make more sense (e.g. game tied with 5 seconds on the clock). I think that we will see 2-pt conversions becoming more commonplace. I also think there will be a good dataset available for this soon with the XFL extra point system. You could also look into play type variation and the 2-point success rate. I seem to recall the dolphins using some pretty crazy stuff around the goal line this year. I would also read this article and perhaps uses it as a framework to analyze this: fivethirtyeight.com/features/when-to-go-for-2-for-real/ I hope this answers your question haha
Thanks for watching! I would check out my data science project from scratch series. In that I have a few examples where I do both of those steps: ruclips.net/video/fhi4dOhmW-g/видео.html. I also have some info on this in my data science fundamentals series: ruclips.net/video/Z9dGmL2G-4k/видео.html
You made my day Sir ! Your video is quite informative and interesting. I would ask you what would you recommend for a freshman economics student to do right now to be a competent data scientist. (I am almost done with all the basic things in R so far.)
@@KenJee_ds I appreciate your reply and thank you so much for it ! I already started dwelling on the projects on Kaggle. I really would be quite happy if I can be a data scientist at Silicon Valley. If one day I achieve that, I will edit this comment. Greetings from Turkey! Stay Home!
Awesome video man!!! Also, I wanted to ask if you could talk about how you would approach building a model for Optical Character Recognition (OCR) bcoz no matter how hard I try I don't understand how to go about it.
Hey Ken, hope you are doing good. Thanks for all the videos. Enjoying it thoroughly. In learning with case studies how do I know which algorithm I should choose? Also, can you upload a project that starts with data collection, which has a lot of missing values in it, where more time is spent on data cleaning and pre-processing as I heard from many DataScientist that they spend the majority of their time in Data preprocessing?
Thanks for watching! In general, if you are trying to predict a number, you use regression. If you are trying to predict a category, you use a classification problem. I recommend watching my data science project from scratch series. In this, I collect the data, clean it, build models, and productionze it with flask. I hope this helps! ruclips.net/p/PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t
Ken are we suppose to memorize all the mathematical formulas in stats? I have this doubt we code for all algorithms then how these formulas help in coding?
Ken, been watching your videos for a few weeks. Really enjoyed the content you put out here. As someone who has gone through the whole switching from non-tech major to data scientist/data analyst, where is the best platform to learn all the basic beside Kaggle? Would Dataquest/Datacamp be a good source? You mentioned project-based learning is the best way to learn data science, besides looking at the project you mentioned above, anywhere else I can start getting my hands dirty?
I really recommend kaggle.com as the starting place. All of the certifications are pretty good too! I have a relationship with 365 Data Science. If you want to try it for a month this is my discount code: 365datascience.pxf.io/NjbEv. They have all the basics, but admittedly, the program isn't as hands on. My best advice would be to think of a problem you want to solve. Then ask yourself if data science can be used to figure out the answer. I find that the best projects come from the most interesting problems. You can also just browse the various datasets on kaggle and see if something speaks to you. I hoe this helps!
Thanks for watching everyone! For more videos on projects, check out these:
The projects that got me a job: ruclips.net/video/imMPnCHvbkY/видео.html
The projects that can get you a job: ruclips.net/video/yukdXV9LR48/видео.html
My Favorite Free Learning Resources: ruclips.net/video/Ip50cXvpWY4/видео.html&ab_channel=KenJee
365 Data Science - Paid Courses ( 57% Annual Discount): 365datascience.pxf.io/P0jbBY
Hi, I hope you are doing great.
Just wanted to know if it is worth putting projects, that are not end to end, on resume (especially when you have no end to end project and are desperately searching for a job due to financial issues at home)?
@@Survivor-xs9gv Yes it is! As long as you show that you understand all parts of the process (even across different projects), you should be fine!
Can the 365 days data science course can land us to an entry level job ???
Will these projects help you get a general idea of what a scientist actually does? To get a feel for whether you like it or not?
Before delving into machine learning I recommend aspiring data scientists hone data cleaning and exploratory data analysis skills. Data familiarization and cleaning being foundational skills. There are smallish datasets that are appropriate for beginners and which can be visually scanned to confirm the findings reported. For example, the automobile fuel efficiency dataset available from UCI contains 'marked' missing values for some numeric data, data entry mistakes primarily in the make and/or model; not every make and model combination is present more than one year, and two fuel types are present. The data source makes available the original dataset as well as a subset still requiring further cleaning, along with a codebook describing the data and each variable. Many instructors tell you to begin by plotting the data but this approach has two problems: you do not know if there are any missing values or other incorrect values which can lead to plots which are erroneous or simply cannot be rendered; and you have not spent sufficient time understanding the raw data and the types of questions it might be able to answer.
I agree! Great advice Gregory!
Great advice
thank you sm
One of the best guy out there, helping beginners to boost through
Thank you for the kind words!!
I too have recently started to learn the basics of data science and honest;y kaggle is one of my favorite sites to find interesting datasets to work with. I took bio as my major in high school and had dropped maths in my last year so the concepts of statistics and probability and linear algebra got me scared at first but after I spent hours completing courses in those subjects (tons of pdfs and youtube channels and online courses) I feel pretty confident in my effort and knowledge. And I cant wait to dig deeper in this fascinating field of data science!
This is awesome to hear! I hope others read this comment!
Good job.....really inspiring
I think the point you make around 6:30 is a really good one in general for all forms of science and engineering. It's really about practice at the end of the day. We're not born math geniuses, we learn math through repetition and problem-solving. Same thing applies to data science and programming. The more you practice it, the better you get and little by little the overwhelming problems seem a little less... overwhelming :)
Glad to hear the point resonated with you! Thank you for calling it out for other viewers. I think it is a really important one!
As a self learner, I'd say your vids are really helpful. First off, there's something calming about your voice. Secondly, the way you guide regarding the projects, helps me set a clear path rather than rush into things and panick. Keep up the God's work your are doing. Thanks!
Thanks for the kind words! Glad the videos have been helpful!
Thankyou so much Ken. When I started to watch your videos, I got an idea about how to start learing data science, Where should I start....😍🥰
"Experiential learning is the best way to learn and go through Data Science", very truly said because that helps us to evaluate where we lack and hence one is able to extend their domain of knowledge. Time to put the pedal to the metal!
Petal to the metal!!
Thanks Ken I'm a spanish computer professional. I'm trying to introduce in ML world to change my career and your videos have help me to center my studies.
Awesome!
This is literally the first video I'm looking on data science, and I was a little bit scared of beggining it, but you gave me a lot of confidence when giving these resources and tips! Thank you m8
Happy I could help Franco! Thanks for watching!
Thanks Ken, once I have finished learning the fundamentals (midway through online course and Kaggle courses), will get these done, your videos are very helpful!
Glad to hear they are very helpful! Feel free to check out the kaggle walk through I just did. It could help with the kaggle familiarity
Thanks for saying "don't get overwhelmed"! Find your videos very helpful. Keep posting, Ken!
Thanks for watching!!!
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Finally we have someone on internet who is really putting you on the right path of learning data science and ML, thanks a tonne! I was completely lost and frustated, I will recommend this channel to my friends and colleagues.
Thank you my friend!
4 Main Project Topics
1. Regression
2. Classification
3. Clustering
4. Deep Learning
I thought deep learning is part of classification, regression
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
@@adiflorense1477 it kinda is in the beginning, but slowly deviates like in case of GANs
Incroyable.
I really want to thank you as I already have done first 2 projects during this lockdown. I realized that everything is possible to learn. You are very good person for sharing the invaluable knowledge.
Thanks for the kind words! I think it is awesome that you've made such great progress during lockdown. keep up the great work!
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
I am just starting out my journey in the field of data science and I find your videos to be really informative and at the same time motivating too.
It was so kind of you to share your personal experience, thoughts and knowledge about what you know to the world.
It really shows how a person's ability lies in their will.
So, I really thank you and wish for great life ahead. 😊😊
Really glad to hear that Nischal! Thanks for watching my videos and for the kind words!
You are most welcome.
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Thank you so much! I cannot believe I didn't realize how helpful Kaggle is
It is an awesome resource! Glad I could help you come to this realization!
One thing I appreciate about your videos is how you tell us about the challenges we will face as beginners but give us the assurance of overcoming these challenges. Thanks for this video. It means a lot to me.
Thanks for watching and leaving such a nice comment!
@@KenJee_ds I’m glad I recieve an early notification from your like and reply. I live in Ghana West Africa and I have knowledge in Python language and I’m now studying data science using books and RUclips videos. Is there a chance I can gain an internship online in the near future while living here in Ghana? I’d love to hear your reply sir ❤️
Hi Ken, I've been watching your videos and really have enjoyed them. I am currently doing an internship in a startup that's developing insect farms (insects for human consumption, mostly crickets). I'm majoring in Industrial Engineering, started my internship in more of a production stand point, but due to the pandemic I've redefined the project for a data driven improvement. In the last couple of months I've helped the company with its data collection and aggregation methodologies and tools. That has led me to be really interested in deepening my knowledge into data science. I'm currently working on the Coursera Data Specialization Courses and will be applying the tips that you've shared in your channel. Keep the good work. Pura Vida!
That sounds like an awesome internship! I would be interested in trying the product. I would love to hear more about it! I am really glad the videos have been helpful to you.
Thank you for the video! It’s always so useful to actually see some real projects that people work on. You got my thumb up
Thanks for watching!!
I started studying Data Science in April, I'm definitely going to be working on these projects. Thanks for this channel it is awesome! I just found it...please continue to make quality content :D
Great stuff! Thank you for watching and feel free to leave any comments if you have questions as you go along!
I started 3 weeks ago and I am lookin at these lol
Through watching your video, I realized that my school DS project, which was to compare error rates of different classification algorithms, won't count as much as the topics you mentioned... I should really work more on new projects. As a recent undergraduate, I want to start with Titanic classification project. And House price regression project sounds fun too! Thanks for the recommendations!
Thanks for watching! I am glad the video helped!
From last one month I am learning data science but I was little bit confused that where should I apply this knowledge but after watching your video now I can do some projects in data science
Thank you.....!😊
Glad I could help! Thanks for watching my videos!
Thanks for the tips, expecially the part were you mentioned that you have passed the fear and it is a natural process of learning, because i am having that feeling now, there are so many things to learn.
Thanks for watching! Learning is a never ending process, might as well make it fun haha
It is very good information, for the people who are new to DS learning. Thank you Ken once again.
the frustration is real. thanks for the encouragement, most times I feel like tossing my computer into the wall. i don't though....too expensive
It can be super frustrating, but I promise it is rewarding!
This is so true! LOL..
Your videos are raw and genuine you have earned a subscriber today.
Awesome! Thanks for watching Vamil!
You're the man, not only highlighting great courses to use and the general way to go about growing in the field but also the words of encouragement for a newbie like me have been extremely helpful. Thank you!!!
Just doing my best to help! Thank you for watching!
Watching in early 2022. I really like this breakdown of process and how to think about solving problems. I'm a musician, producer and recording engineer and a lot of these....learn-by-doing and divergent thought processes remind me a lot of how to get a good mix or composing. Really excited on this journey
Awesome!! I love seeing parallels between domains!
upvote for sports analytics projects!
😃
Thank you for the video. I can now see a way to begin. I always thought when doing a new thing, where to start is the most difficult step.
Glad to hear!
Really like your content Ken ! watch your videos everyday.
Thank you for watching! I am glad you are enjoying the videos!
This is a great help, I am a bachelors student and I really want to be able to give value in future. I’ve been wondering how to start learning by experience and this video encouraged my search. Thanks.
Happy to hear this helped!
Thanks to you .From two weeks I was confused where to start in kaggle keep it up👍
Glad to hear! Hopefully my new video on the kaggle titanic dataset will be useful as well!
I thumbed up and watched the video all the way through, why? because you also listed the things out in the description. Most people want to clickbait you into watching the full video so they dont list the content in the description, usually we have to go to the comments for someone to list them up. So thank you its appreciated.
I appreciate you watching it through! I hope that the content was valuable to you as well!
Great content man, I'm from Peru and I can't stop watching
Thanks for watching Jesus! It makes me really happy that you're enjoying the videos!
Ken, so much clarified in such a short video.
Thank you.
Glad it was helpful, thank you for watching!
I recently started learning data science and started on this kaggle project of House hold prices. I was trying to establish relationship between the time at which houses are sold to the sale price of houses
Awesome!
Ken Jee But i have hit a dead end. Can we really apply time series relation on this data ?
Love the "Modular, Git-R-Done" approach of Kaggle learn and these projects!
I hope that was a git / R programming language pun! Thanks for watching Dylan!
Hi Ken. I totally relate with everything you’ve said. I’m currently in grad school and it’s a struggle grasping the concepts and being expected to apply these concepts towards successfully completing the program
Hope your projects are helping to cover the gaps! I was very reliant on them for my own learning during grad school
Thank you for the recommendations.
Thanks for watching!
I wonder how much speaking the word "algorithm" helps out with the algorithm
I hope it helps... algorithm
@@KenJee_ds algorithm
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Thank you for your helpful tips. Nice, to the point brief. Keep updating.
Thanks for watching!
This is really helpful. Thank you Ken
Thanks for watching Mubarak! Glad you found it helpful!
i like to watch your videos...
being a lecturer and learner its really helps me to catch right path for learning
all the best..love from INDIA
Thank you for watching Rohini!
hey man thanks for the motivation to push on
You can do it!
Thanks, I'm glad this was helpful
One of the best channel on RUclips for data science . Lots of love man 😘 .ur approach is very practical.
Thanks for the kind words!
Good luck with your channel.
I'm trying to learn data science by myself and Ive been told I'm a quick learner but then I know that I lose interest or motivation even more quickly and have been at this for quite some time without much progress.
I think I should go with the projects and learn alongside, hope it works.
I agree! I think going with projects that you are excited about is the best approach to stay motivated! I also made this video that may help with motivation: ruclips.net/video/akbU9KOo_Qc/видео.html
Thanks for the amazing efforts you put on this..
Will start with the housing
Thanks for watching! That is a good one to start with!
This is what I'm looking for, thanks
Great!
Great advice, Thank you very much, Ken.
Thanks for watching!
Geez this is really good
Thank you!
Nice! Having joined Kaggle recently, and due to one of your recommendations (thanks and kuddos by the way :D ) ... I feel that I am now ready to start these kind of projects. Since I did followed successfully the two first courses of Frank kane, it seems like a natural progression for me.
Glad to hear this was helpful! Feel free to follow along with my walk through of the titanic dataset that I published recently!
@@KenJee_ds I saw that you had a video about it and will definitely check it out ;)
That being said, I'd like to blunder with it a little ... and blunder like making mistakes in the safety of my home ... that kind that come with valuable lessons and everything, after which I'll check your video to compare ;)
Also, it will probably gave me a hint as to where my weaknesses are so I can plan my studying accordingly.
Anyways, thanks for the channel! As far as I can see, it's full of useful stuff for a beginner like me.
The iris dataset is ideally suited for regression, classification, and clustering projects for beginners. Working with a single dataset and learning which types of machine learning algorithms are possible and appropriate can reduce the time before you can tackle resume-worthy projects. Another useful skills is the ability to compare approaches (regression vs. classification vs. clustering) so you are ready to make recommendations to your manager or client or even your team about why a certain approach or algorithm is better-suited to the task at hand.
Thanks for the thoughts here Gregory!
your videos really help alot
Thanks for watching! It makes me happy that you find them helpful!
Thanks Ken for making this video. 😊
Thanks for watching!
Really great video! Just what I needed. Thanks 👍
Thanks for watching Tejas!
I love your ideas. This is a great guide on how to start.
Thanks for watching!
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Thank you! This video is really useful.
Glad it was useful, thank you for watching!
Very helpful video man. You've gained a sub
Thanks Sohail! Glad you found it helpful!
Excellent content, thanks!
Thanks for watching!
Thank you, that's great video!
Thanks for watching Ridwan!
Great Content.
You really explain things in layman terms. Thankss!
Really glad to hear! Thanks for watching!
Love your channel man! I'm gonna try these out!
Awesome! Would love to hear how they go!
Such an amazing channel and great content. Thank you for sharing!
Thanks for watching!
Here's a course you need, it's paid but it's worth it.
khadymschool.thinkific.com/courses/data-science-hands-on-covid-19-face-mask-detection-cnn-open-cv
Superb dude!!Thank you
Thanks for watching!!
Thanks for the tips!
5:44 I believe MNIST images are 28x28. So it would be 784 pixels per image
Good catch! Thanks!
Thanks for your content!
Thanks for watching!
Thanks ken, absolutely great videos :)
Thank you for watching them Andri!
Outstanding video ...love the way you break it down to the bare essentials , as to how we should approach the development of this new set of skills... :-)
Thanks for watching and for the kind words!
Hi, Ken. Can you make a vlog about your hardware setup? Great content, btw. Keep it up!
I am planning to! I am in the process of moving, so I will do that once I get things set up in the new place!
Mind-blowing the way to learn data science that u have given. Thanks a lot 😊 more video that ll be helpful for us
Thank you for the kind words and for the support! Glad it was helpful!
I must add that your teaching Algorithm is 95% accurate..... I would have written 100% but that will make your model ( your teaching ) over fit...you know what I mean...
Haha thank you! I will take 95%!
Perfect.video.for.anyone to start off in Kaggle with a great learning practice path. Thanks
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Thank you, very interesting!
your videos are very helpful!
Thanks for watching!
Thank you for the content, I am right at this stage.
Awesome! Glad this was helpful. Good luck getting to the next stage!
Thanks
Good stuff! Thank you for sharing. I am taking online courses and your videos give me helpful insights. I also like the short length of your videos which is very efficient. Thanks!
Glad to hear the videos are helpful Arash! THanks for watching!
Clear as water, thanks
Glad to hear! I hope it helped!
Thank you, you are great
Thanks for the quality content!
Thanks for watching Jack!
Amazing information ken!
Thanks for watching!!
Ken Jee I am trying to land a job in the field but so far I have been unsuccessful, do you have any tips?
@@icvsmiglani8790 The biggest two things I recommend are 1) building your portfolio out more 2) networking to get a employee referral. This increases your chances by more than 5x! I actually have a course with 365 data science on how to break into the field if you're interested, I go into quite a lot more depth there. I also have a few interviews on my channel with tina huang and data leap where we talk about these topics in depth. I recommend checking those out!
Awesome video. Its so helpful for beginners like me.
Glad to hear! Thank you for watching!
love it!
Thanks for watching!
I am a physicist, and I need to learn data, science, and machine learning for experimental physics, material science, 2D material Identification (their quantum properties, and so on). as It seems, I must learn it if I wish to survive in my knowledge domain.
This clip is your best clip that suits me 😎thx bro
Thanks for watching! Glad it was helpful!!
@@KenJee_ds i,m coming from #66ofdata. 🤗
@@ai.simplified.. Awesome!!! Thanks for being a part of that too!
Thanks for this video Ken. I have a sports analytics problem for you. When the 3 point shot was first introduced in the NBA, I said start launching them. Traditionalists said, oh no, too risky, too many will ruin the game. 20+ years later 3 point shots are an integral part of any teams' strategy. OK, my question is regarding the 2 point conversion in football. My belief is that the 2 point conversion should be tried every time. Once a team gets used to it they will develop special teams just for the conversion. Plus, once fully developed, these plays could be used for any regular down inside the 5 yard line. The amount of dedicated plays could be unlimited - 2 QB's on the field, 5 running backs, non-traditional formations, etc. Not to mention how exciting the game would become. Do you have any supporting evidence that, over time, going for the 2 point conversion 100% of the time would pay off? I guess part of your problem is that you cannot measure what hasn't been tried.
To be honest, football is not my area of expertise, but I am a firm believer that coaches go for two far less than they should. There are however, some situations where I think going for the extra point will always make more sense (e.g. game tied with 5 seconds on the clock).
I think that we will see 2-pt conversions becoming more commonplace. I also think there will be a good dataset available for this soon with the XFL extra point system.
You could also look into play type variation and the 2-point success rate. I seem to recall the dolphins using some pretty crazy stuff around the goal line this year. I would also read this article and perhaps uses it as a framework to analyze this: fivethirtyeight.com/features/when-to-go-for-2-for-real/
I hope this answers your question haha
Thank you, Data Professor.
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Thanks for tips!
Thanks for watching!
Thank you so much! :)
Thanks for watching!
Awesome video for beginners!!! thanks for your insight! do you have videos on steps of data exploration and preparation as well?
Thanks for watching! I would check out my data science project from scratch series. In that I have a few examples where I do both of those steps: ruclips.net/video/fhi4dOhmW-g/видео.html. I also have some info on this in my data science fundamentals series: ruclips.net/video/Z9dGmL2G-4k/видео.html
Thank you!!
Thank you for watching!
great . . . thank you
Happy I could help!
thanks alot ken
,would you tell me what i have to learn(tools) to be able to go through this three projects
You made my day Sir ! Your video is quite informative and interesting. I would ask you what would you recommend for a freshman economics student to do right now to be a competent data scientist. (I am almost done with all the basic things in R so far.)
I would start doing projects as soon as you can! I think this is the best way to learn the field and build out your resume!
@@KenJee_ds I appreciate your reply and thank you so much for it ! I already started dwelling on the projects on Kaggle. I really would be quite happy if I can be a data scientist at Silicon Valley. If one day I achieve that, I will edit this comment. Greetings from Turkey! Stay Home!
Awesome video man!!!
Also, I wanted to ask if you could talk about how you would approach building a model for Optical Character Recognition (OCR) bcoz no matter how hard I try I don't understand how to go about it.
Thanks for watching! I actually have no experience with OCR haha. I may try to do a project using it later this year though. Stay tuned for that!
Thanks 🙏🏻👌🏻💯
Thanks for watching!
Hey Ken, hope you are doing good. Thanks for all the videos. Enjoying it thoroughly. In learning with case studies how do I know which algorithm I should choose?
Also, can you upload a project that starts with data collection, which has a lot of missing values in it, where more time is spent on data cleaning and pre-processing as I heard from many DataScientist that they spend the majority of their time in Data preprocessing?
Thanks for watching! In general, if you are trying to predict a number, you use regression. If you are trying to predict a category, you use a classification problem. I recommend watching my data science project from scratch series. In this, I collect the data, clean it, build models, and productionze it with flask. I hope this helps! ruclips.net/p/PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t
@@KenJee_ds Thanks Bro👍will definitly watch
Ken are we suppose to memorize all the mathematical formulas in stats? I have this doubt we code for all algorithms then how these formulas help in coding?
Ken, been watching your videos for a few weeks. Really enjoyed the content you put out here.
As someone who has gone through the whole switching from non-tech major to data scientist/data analyst, where is the best platform to learn all the basic beside Kaggle? Would Dataquest/Datacamp be a good source?
You mentioned project-based learning is the best way to learn data science, besides looking at the project you mentioned above, anywhere else I can start getting my hands dirty?
I really recommend kaggle.com as the starting place. All of the certifications are pretty good too! I have a relationship with 365 Data Science. If you want to try it for a month this is my discount code: 365datascience.pxf.io/NjbEv. They have all the basics, but admittedly, the program isn't as hands on.
My best advice would be to think of a problem you want to solve. Then ask yourself if data science can be used to figure out the answer. I find that the best projects come from the most interesting problems. You can also just browse the various datasets on kaggle and see if something speaks to you. I hoe this helps!