What is Machine Learning? Basics Explained in Tamil
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- Опубліковано 24 лип 2024
- In this video I explain the basics of Machine Learning and how it works with an example of Neural Network.
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Contact me: csintamil42@gmail.com
Disclaimer: Everything I say in this video is my own opinion and not that of my employer or any other organization.
Timeline:
00:00 Introduction
01:33 Why we need Machine Learning?
03:36 Neural Networks
09:08 Normal Programming vs Machine Learning
15:25 Uses in real world
18:58 Conclusion
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i am Abinesh, from Srilanka. good explanation Bro. Thank you!
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Congrats!
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Thank you so much for this topic bro. Romba naala intha topics kutha wait panitu iruntha. Do more videos bro with python example❤️😊.
Such a great explanation no doubts for machine learning I like your videos please upload a more topic from this machine learning algorithms and keep it up then improve your video quality keep it up thank you brother
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Explaining Quality 100% accuracy 💯
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Nice and simple explanation on ML. But when writing ML models there are a lot of frameworks like tensorflow or pytorch that uses tensors with best time complexity. CNN(Convolutional neural network) is used in image classification. The process goes like this Input data -> Preprocessing of the data -> Choosing the parameters for the model(Hyper parameter tuning) -> training the model (i mean training the CNN model itself) -> check the confusion matrix if it is a classification problems or check the RMSE and R2 values if it is a regression problem. if the confusion matrix has more false positives and true negatives then tune the hyper parameters more or pre process the data differently until the false positives and true negatives are minimum. The CNN models are programmed using tensors and CUDA to provide performance. Please check out Tensorflow and Pytorch. To be honest it is very difficult to determine how the model is classifying the image.
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i would suggest you to start with naive bayes. it is used in email spam classification. Simple ML Model based on conditional probability.
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A set of descriptors distribution convergence curves. Or properties or characteristics.
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Your please put the mathematics associated with machine learning . Like linear algebra ,vectors, calculus ,differentiation,integration , statistics , probability etc. Not just Python or other languages packages supporting machine learning immediately upon a given machine learning requirements .அப்பதான் training முழுமையடையும் . I am an experienced java developer with some bigdata hands-on . But no machine learning handson
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False negatives and false positive. Kinda reverse la weight adjust agum. So you can get accuracy better. Kaggle use pannga for learning. Based on confusion matrix
True, confusion matrix is the way to determine whether the classification is working or not.
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