Data Science 101: Overview of Machine Learning Model Building Process
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- Опубликовано: 11 янв 2020
- Are you just starting out data science and are looking for an introductory video on the concepts of what it takes to build a machine learning model. Look no further, in this video we cover the basic concepts of the machine learning model building process. The concept of this video first started out as a drawn infographic and is now converted to a video format.
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Inspired from our own infographic "1 page summary of the machine learning model building process" and the suggested comment from Bazi Ahmed
📎INFOGRAPHIC: doi.org/10.6084/m9.figshare.1...
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🤔QUESTION OF THE DAY: What topics in data science would you like to be covered in a future video? Comments down below! 😃
💗Help support this RUclips channel by hitting the Subscribe button, Like button and Comment down below! 👇
Data Professor A code walkthrough of Exploratory data analysis using PCA, SOM, Statistics preferably in python would be great!
Prasant Kumar Thanks for the comment and support. Sounds like a great idea, will definitely keep this in the list of to-do.
Data Professor Coverage on Deep Learning would be great!
Data science in cloud computing by details .. thanks a lot sir
Okay so I am basically a Biotechnology student and other than Biotechnology, I understand only
Bioinformatics and little bit of Python programming,
I am interested in learning Data science and Data analysis, data visualization for which I have been watching all your videos which are pretty helpful. In this video, I got little bit confused about the difference between Training set and Test set, you said those prediction models can be used as a training set as well as Test set, it will be helpful enough if you elaborate and put some more information on the algorithms and how can they be optimized
Thank You
Hands down the most efficient and precise explaination, I literally came here after I cant understand it from my coursera course but watching this video cleared the doubt which was taking me hours of videos and yet feeling clueless❤❤
Thanks to your recommendation of learning data mining with Weka and the knowledge of statistics that I was teached by my studies in industrial engineering I really understood your explanation, thanks for sharing.
Greetings from Dominican Republic.
wow, you just earned a sub. very well structured and clearly spoken and relayed. looking for the video on deployment now.
Thank you for the kind words 😃
This was fantastic! What a great overview. Thank you!
Glad you enjoyed it!
Great video, thanks for posting this. I'm new to data science and this is exactly the type of thing I was looking for. Subbed here and liked on FB.
Thank you very much Jared Myers for the kind words and for your support, I’m glad that you found the video useful.
I literally just followed this summary for my first machine learning project and it was great !! I ended up with a good model after trial and errors , I never thought that I will be ending up into the realm of machine learning
Glad to hear, welcome to machine learning!
Love the content, with you by my side, I know I can become a data scientist
Hey thanks, glad the contents are helpful 😊
Amazing explanation sir ❤thank yousm
Great! Thank you so much for this visualization! This is an excellent summary of my separate pieces of knowledge taken from Coursera.
Glad it was helpful!
Hey Chanin, I recently saw on LinkedIn your illustration of data science from model development to cross validation. Great, useful visual! I also subscribed to learn more. Thanks! I am hoping to get accepted soon to a few data science graduate programs. - Alex T
Glad, you've found it useful and thanks for your support and for subscribing. Best of luck with your grad school applications.
I missed this. Thanks for the illustration, it is very informative.
Glad you enjoyed it!
In addition to performance evaluation metrics for classification, you can look at the ROC curve/AUC, logarithmic loss, f-1 score, and Chi-Squared.
Yes, definitely, thanks for the comment 👍👍
many many xD
Awesome Explanation!!
Glad it helped!
Thank you for sharing krub. This is very informative.
It is a pleasure krub. Thanks for your comment and for your support.
Awesome explanation! Please keep producing more videos & contents!:D
Thanks for the encouragement 😊
Great explanation
Great stuff!!
Thanks
It's so cute! I feel like this is just what I also want to devote myself to!
Love data professor. I took a few classes, none of them close to data professor
Glad to hear that you've found it helpful :)
Great video!!
Thanks!
this is so comprehensive, i just subscribed to your channel, and definitely will like your facebook page too!!
Welcome aboard! Thanks for watching!
Very informative. Can you do a video how we can pick the right model for our projects. Thanks and happily subscribed!
Thank you. Subscribed
Awesome, welcome!
Hey Data Professor, thank for making this video. It is a great lecture which educates me on what a machine learning process looks like. Your explanation is thorough and precise so I really appreciate that. I would like to be a Data Scientist in my career in the long term but I will start with working as a Data Analyst. Do you think what is the biggest difference between a data analyst and data scientist? And does a junior level data analyst use lots of ML generally?
Thanks Steve for the comments. Sounds like a great topic for a future video. Briefly, I would say data analyst would be a stepping stone to becoming a data scientist in the long run. Mostly data scientist positions are those with advanced degrees and more technical skills in math and programming to implement the ML workflow. Both require passion to gain insights from data and translate that into actionable data-driven decisions of the organization.
Fantastic ! More pls 😃
Thanks, this is another one ruclips.net/video/NRnaMCNOK7Y/видео.html
Nice overview and explanations via the infographics. Very easy to grasp the concepts this way. Any plans to cover the topics using python?
Prasant Kumar Thanks for the comments and your support, yes Python is in plan for future videos.
Data Professor Thanks, good to know that. A codewalkthrough (preferably in python) explaining all the steps you mentioned in the ML model building video would be fantastic!
Great overview! 🙏🏼 What does the algorithm "dt" stand for?
Thanks for your comment. Here's the full names for the acronyms. DT = Decision Tree, RF = Random Forest, KNN = K-Nearest Neighbors, GBM = Gradient Boosted Machines, DL = Deep Learning, SVM = Support Vector Machine, PCA = Principal Component Analysis, SOM = Self-Organizing Map, MCC = Matthews Correlation Coefficient, RMSE = Root Mean Squared Error, MSE = Mean Squared Error, R2 = R-Squared
@@DataProfessor thanks 🙏🏼
Kindly Upload overview on probability and Statistics!! It will be very helpful!! Thanks in Advance!!
Thanks for suggestion. I will definitely consider that for future videos but in the meantime you want to check out the excellent channel by Josh Starmer (Stat Quest) at ruclips.net/channel/UCtYLUTtgS3k1Fg4y5tAhLbw
@@DataProfessor Thanks a lot!!
@@kanimozhipanneerselvam3017 😃
Hey Chanin, I am interested in creating such 'infographics' too. Can you share the site from which you created this masterpiece? Thanks!
Yes, sure, I use the GoodNotes app on an iPad to draw the infographics.
@@DataProfessor Thanks!
How much the model development process has changed since 2020? Is there anything that need to be emphasized nowadays?
Hi Chanin, I want to extract address from unstructured data. Can you help me how to create an own model to achieve this?
Hi Santosh, I would recommend to look into using natural language processing for this. A google search for "nlp address extraction" (without the quotations) would be a great way to start.
Hello professor! I don’t use FB. Where can I get the image of ML steps
Yes, sure I shared it here github.com/dataprofessor/infographic/blob/master/01-Building-the-Machine-Learning-Model.JPG
Hi professor, could you indicate a book that describes this whole process in detail? Thanks!
Sure, Nathalia, can I recommend 2 books? The first is Data Science from Scratch (learning.oreilly.com/library/view/data-science-from/9781492041122/) and the second is (www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)
@@DataProfessor Thank you very much!!
@@ncruves Nathalia, You're welcome!
Great video. Very useful. Can you give me code of this model. I take it like examples.
Thank you so much
Hi, the infographic is available for download at figshare.com/articles/figure/Building_The_Machine_Learning_Model/11492316
syntax syntax syntax i can always look into the documentation
Btw, in the links that you posted under **Follow Us**, dataprofessor.org brings me to a dead site. You might also want to consider adding the link to your FB page there instead to make it more convenient for users to head straight over there :)
Thanks so much for the pointer, how did I miss putting up the Facebook link? It’s added now and I also put in parenthesis that the website is under construction, will get it up and running as soon as possible. Have a great day!
Great video, very helpful. Could I have your email id, please??
sure its dataprofessorofficial@gmail.com
@@DataProfessor Thank you sir!