Can't thank the 365 Data Science team enough for all the effort they put into providing us with quality free content. Thank you 365 team for continuing to bridge the gap in accessesiblity to education!
this is indeed a well rounded introduction to machine learning. what l have learnt from this course is this: In normal programming we get our output or results by giving out inputs some set of mathematical instructions to.follow. But in machine learning, we give the system inputs and their corresponding historical outputs so that the system can learn the set of rules or instructions required for future inputs and outputs.
I just want to say you guys are awesome❤️💯✨ ..your video really helps me in learning data science and machine learning as I am very beginner and I have never did data science or machine learning before ..best channel for data science students ❤️💯
Frankly speaking, words are not enough to describe this type of content from 365 team. Previously, I have explored about ML in other sites,blogs etc etc., but, none of them can match this one. And, this is where I learned perfectly and moreover clearly about the basics of Machine Learning. I highly recommend everyone to go through this video.(If you want to know about basics of ML from scratch). Dont waste time in exploring other sites,pages etc etc Just watch this video and even remaining 365 Data Science videos too.They all are damn good.
365 data science has explained these confusing concepts in a normal layman's language. Liked the way you guys explained supervised, unsupervised and reinforcement learning. Thank you for making our DS transition journey fruitful. Looking for more such videos.
A lot of things go into machine learning, a good base in probability and statistics is actually needed. We must understand the inputs and be confident enough to manipulate the data thoroughly to meet our needs.
Thank you guys for the great content you offer! The Data, Model, Objective Function, and Optimization Algorithm workflow of building a Model that you have shown in this video can both be applied to Machine Learning and Deep Learning Algorithms as well! I have completed your course "The Data Science Course 2020: Complete Data Science Bootcamp", in addition to "Customer Analytics in Python", two amazing courses! Please pick me as one of your Winners for the 1 Free Month on your amazing platform with amazing content!! I can really make use of what you are offering. Good luck!
Wow! this Machine learning introduction inspires me to learn more about it and solving problems that would contribute to making the world a better place. Thank you 365 Data Science for this little opportunity, hoping to learn more😊
There is a mistake at 18:30 in the representation of w matrix. The first row should be with the positive numbers, the second with the negative numbers. The x's row multiplies with columns not with rows. Nevertheless, the result is correct.
brilliant content. wanted to confirm if the weights shown at 17:19 are in correct position -15,512 should be in row 2 col 1 instead of row 1 col 2 in the video ?
Sir if we have hundreds of columns without the name. Then how can we name them using pandas and a for loop or lambda function because if we try to name them using names=[] it will be a very time-consuming process. The name of the columns can be col1, col2 , col3...etc.
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Can't thank the 365 Data Science team enough for all the effort they put into providing us with quality free content. Thank you 365 team for continuing to bridge the gap in accessesiblity to education!
All your courses are always awesome. Thanks alot.
this is indeed a well rounded introduction to machine learning. what l have learnt from this course is this: In normal programming we get our output or results by giving out inputs some set of mathematical instructions to.follow. But in machine learning, we give the system inputs and their corresponding historical outputs so that the system can learn the set of rules or instructions required for future inputs and outputs.
I just want to say you guys are awesome❤️💯✨ ..your video really helps me in learning data science and machine learning as I am very beginner and I have never did data science or machine learning before ..best channel for data science students ❤️💯
Frankly speaking, words are not enough to describe this type of content from 365 team.
Previously, I have explored about ML in other sites,blogs etc etc., but, none of them can match this one.
And, this is where I learned perfectly and moreover clearly about the basics of Machine Learning.
I highly recommend everyone to go through this video.(If you want to know about basics of ML from scratch).
Dont waste time in exploring other sites,pages etc etc
Just watch this video and even remaining 365 Data Science videos too.They all are damn good.
365 data science has explained these confusing concepts in a normal layman's language. Liked the way you guys explained supervised, unsupervised and reinforcement learning. Thank you for making our DS transition journey fruitful. Looking for more such videos.
A lot of things go into machine learning, a good base in probability and statistics is actually needed. We must understand the inputs and be confident enough to manipulate the data thoroughly to meet our needs.
Thank you guys for the great content you offer! The Data, Model, Objective Function, and Optimization Algorithm workflow of building a Model that you have shown in this video can both be applied to Machine Learning and Deep Learning Algorithms as well!
I have completed your course "The Data Science Course 2020: Complete Data Science Bootcamp", in addition to "Customer Analytics in Python", two amazing courses!
Please pick me as one of your Winners for the 1 Free Month on your amazing platform with amazing content!! I can really make use of what you are offering.
Good luck!
I have recently started learning Machine learning. Using kaggle and 365ds for basics.
Wow! this Machine learning introduction inspires me to learn more about it and solving problems that would contribute to making the world a better place. Thank you 365 Data Science for this little opportunity, hoping to learn more😊
There is a mistake at 18:30 in the representation of w matrix. The first row should be with the positive numbers, the second with the negative numbers. The x's row multiplies with columns not with rows. Nevertheless, the result is correct.
I saw the same error! The W matrix is transposed. Is more natural if you do y = Wx + b, being y and b column vectors
I noticed the same mistake but I wasn't sure , thanks for the correction
I am always waiting to see your new videos and I hope to learn from your excellent courses! Thank you a lot!
This was only the intro and it's already over my head.
Thanks 😊
Great efforts! Great thanks!
l am a indeed a huge fan of your product offering and l must say it is indeed a great place for beginners to get their hands dirty with data science.
brilliant content.
wanted to confirm if the weights shown at 17:19 are in correct position -15,512 should be in row 2 col 1 instead of row 1 col 2 in the video ?
Very nice introduction, thanks for making this content available!
Yeah u really do great job..This is great introduction to Machine learning....
This video is amazing and th introduction is full of knowledge , thanks for sharing this with us .. keep going in making things easier 👍
Which is more important - Model Accuracy or Model Performance?
Thanks for the free content guys. ☺️
I reckon it's Model Accuracy.
@@ThErrandBoy Okay.
Hello @365ds you are amazing i understood that much from and thank you so much for making this useful video i really liked it
A good introduction to ML. thanks Jamuna
Is this the first chapter . I am a n experienced java web developer with big data and some python experience . I want to learn machine learning
Kiriman dari dia untuk machine learning yaitu : supervised termasuk classifasi dia.,machine learning dia.
365 has helped so much, Im a chemical engineering trying to transition into data science and machine learning field!
Sir if we have hundreds of columns without the name. Then how can we name them using pandas and a for loop or lambda function because if we try to name them using names=[] it will be a very time-consuming process. The name of the columns can be col1, col2 , col3...etc.
Man, I wish I had started learning machine learning a year ago
do 365 offering financial aid to those who can't afford ?
Time Stamps? Anyone?
Please upload full course
Hmmm
Please make a full tutorial