Data science/ML/AI

The Hundred-Page Machine Learning Book by Andriy Burkov 📄 152 pages 🔗 Book link #machinelearning #ml ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @programming_books_bds for more

A LITTLE GUIDE TO HANDLING MISSING DATA Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀 How can you handle these missing values, ensuring you dont loose important part of your data🤷‍♀️ Not a problem😌. Here are important facts you must know😉 ✍️Instances with missing values for all features should be eliminated ✍️Features with high absence rate should either be eliminated or filled with values ✍️Missing values can be replaced using Mean Imputation or Regression Imputation ✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances ✍️Regression Imputation might overfit your model ✍️Mean and Regression Imputation can't be applied to Text features with missing values ✍️Text Features with missing values can be eliminated if not needed in data ✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized

labmlai/annotated_deep_learning_paper_implementatios This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations Creator: labml.ai Stars ⭐️: 7.8k Forked By: 703 GithubRepo: https://github.com/labmlai/annotated_deep_learning_paper_implementations ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data Preprocessing: Understanding and Detecting Outliers Here's a guide to understanding, detecting and handling outliers👀. I hope you gain the confidence you need to handle them😁 Outlier Detection and Analysis Methods Link: Click Me 😌 Detecting and Treating Outliers | Treating the odd one out! Link: Click Me 😌 Python Treatment for Outliers in Data Science Link: Click Me 😌 Why You Shouldn’t Just Delete Outliers Link: Click Me😌 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Let's talk about some simple stat terms - mean, median and mode Mean, median, and mode are three kinds of "averages". There are many "averages" in statistics, but these are, I think, the three most common, and are certainly the three you are most likely to encounter in your pre-statistics courses, if the topic comes up at all. The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers. The "median" is the "middle" value in the list of numbers. To find the median, your numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median. The "mode" is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list. Task: Find the mean, median, mode, and range for the following list of values: 13, 18, 13, 14, 13, 16, 14, 21, 13 Solution: mean: 15 median: 14 mode: 13 Explanation: The mean is the usual average, so I'll add and then divide: (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) ÷ 9 = 15 The median is the middle value, so first I'll have to rewrite the list in numerical order: 13, 13, 13, 13, 14, 14, 16, 18, 21 There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number: 14 The mode is the number that is repeated more often than any other, so 13 is the mode.

Machine learning for dummies IBMs limited edition Judith Hurwitz Daniel Kirsch https://www.ibm.com/downloads/cas/GB8ZMQZ3

Awesome Public Datasets for Your Projects This contains numerous datasets ranging from : Agriculture Biology Climate+Weather Complex Networks Computer Networks Cyber Security Data Challenges Earth Science Economics Education Energy Entertainment Finance ... There's alot you can lay your hands on here Stars⭐️: 48.8K Fork: 8.7K Repo: https://github.com/awesomedata/awesome-public-datasets ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Facts you need to know about GPUs for Deep Learning Have you heard about GPUs?🤓 What is GPU and why should i care?🤨 Well I know you might be wondering what this has to do with your deep learning projects😉 Graphics Processing Units (GPUs) are specialized processing cores that you can use to speed computational processes. It was initially designed to process images and visual data. But now, It is used in reducing the efficiency and power needed to run DL projects, 👌It enables the distribution of training processes and can significantly speed machine learning operations. 👌It is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations. 👌Training models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly. 👌It has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.

Structured vs unstructured data It is useful to distinguish between structured and unstructured data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about semi-structured data, that have some sort of a structure that may vary greatly.

K-Means clustering explained

20 AWESOME SOURCES OF FREE DATA SETS If you are after solid data to do your projects with ease and lessen the stress of doing the data collection yourself, here's a good resource containing amazing sites where you can get your data sets for free😁 https://www.searchenginejournal.com/free-data-sources/302601/#close