Data Science 101: Overview of Machine Learning Model Building Process

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.

Inspired from our own infographic "1 page summary of the machine learning model building process" and the suggested comment from Bazi Ahmed

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[DataProfessor]

🤔QUESTION OF THE DAY: What topics in data science would you like to be covered in a future video? Comments down below! 😃
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[nersha]

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❤❤

[rolimiranda]

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

[AngelFelizF]

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.

[travelclimb]

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

[fatimak]

wow, you just earned a sub. very well structured and clearly spoken and relayed. looking for the video on deployment now.

[machine_learning_engineering]

In addition to performance evaluation metrics for classification, you can look at the ROC curve/AUC, logarithmic loss, f-1 score, and Chi-Squared.

[CrazyFanaticMan]

Love the content, with you by my side, I know I can become a data scientist

[mankomyk]

Great! Thank you so much for this visualization! This is an excellent summary of my separate pieces of knowledge taken from Coursera.

[katwoods]

This was fantastic! What a great overview. Thank you!

[DrSteelerNation]

Love data professor. I took a few classes, none of them close to data professor

[JJ-iope]

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.

[minicorefacility]

I missed this. Thanks for the illustration, it is very informative.

[stevezhu]

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?

[elainee]

Awesome explanation! Please keep producing more videos & contents!:D

[anujvyas]

Hey Chanin, I am interested in creating such 'infographics' too. Can you share the site from which you created this masterpiece? Thanks!

[urfavchickenlegs]

this is so comprehensive, i just subscribed to your channel, and definitely will like your facebook page too!!

[prasantkumar]

Nice overview and explanations via the infographics. Very easy to grasp the concepts this way. Any plans to cover the topics using python?

[pattarachair.]

Thank you for sharing krub. This is very informative.

[biniambelay]

Very informative. Can you do a video how we can pick the right model for our projects. Thanks and happily subscribed!