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🤓 ML Cheatsheets! Supervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf Unsupervised Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf Deep Learning: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf ML Tips/Tricks: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf Stats/Prob: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf Linear Algebra: https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf Save for later, share with tech-circle, stay subscribed for more! 😎

The Cheatsheets of deeplearning is shared on LinkedIn. https://www.linkedin.com/posts/abhishek-kumar-singh-8a6326148_cheatsheets-for-deep-learning-activity-6831565210227134464-NKiv

Some interview questions related to Data science 1- what is difference between structured data and unstructured data. 2- what is multicollinearity.and how to remove them 3- which algorithms you use to find the most correlated features in the datasets. 4- define entropy 5- what is the workflow of principal component analysis 6- what are the applications of principal component analysis not with respect to dimensionality reduction 7- what is the Convolutional neural network. Explain me its working

TLA: Twitter Linguistic Analysis - TLA is built using PyTorch, Transformers and several other state of the art machine learning techniques and it aims to expedite and structure the cumbersome process of collecting, labeling, and analyzing data from Twitter for a corpus of languages while providing detailed labeled datasets for all the languages. $ pip install TLAF https://github.com/tusharsarkar3/TLA

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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales. Let's talk about it 🧵 There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are: ▪️ Normalization ▪️ Standardization The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses. Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1. This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value. In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance). More about them: ▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms. ▪️Standardization is robust to outliers. ▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2. Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works. https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data. https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key. Enable gradient descent to converge faster