Data science/ML/AI

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SQL Free Resources Looking to learn SQL for free? Here is a curated list of websites you can use to upgeade your SQL skill level or practice writing queries. Remember SQL is a necessary skill to have in your toolkit as a data professional. 1. W3 Schools https://w3schools.com/sql 2. SQL Zoo http://sqlzoo.net 3. SQLBolt http://sqlbolt.com 4. Khan Academy https://khanacademy.org/computing/computer-programming/sql 5. FreeCode Camp https://youtu.be/HXV3zeQKqGY To Practice what you have learned and build your skill at hte same time , you can use these: 6. Hacker Rank https://hackerrank.com/domains/sql 7. SQL Murder Mystery Game https://mystery.knightlab.com #datascience #SQL ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Where to find Data for Machine Learning High quality data is key for building useful machine learning models. Models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products. This article gives a concise explanation on finding the right data for your models. https://towardsdatascience.com/where-to-find-data-for-machine-learning-e375e2a515c8

Statistics Guide for Data Science Learning Statistics for Data Science can be quite overwhelming for beginners without a Statistics background. One can get confused on which topics to learn or books to read up to equip their knowledge You don't have to learn it all. Here are essential topics you can learn 1) Know what a p value is and its limitations 2) Linear Regression and its Assumptions 3) Different Statistical Distributions and when to use them 4) Mean, Variance for Normal, Poisson, and Uniform Distribution 5) Sampling Techniques and Common Designs(eg: A/B) 6) Bayes Theorems and it's application 7) Measurements and Interpretation of Confidence Intervals 8) Logistics Regressions and ROC curves 9) Resampling(Cross Validation and Bootstrapping) 10) Tree Based Models ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Head First SQL Here's a brain friendly guide to learning SQL for beginners Author:Lynn Beighley Pages: 586 Link: Click Me!

Amazing Free Resources on Data Science and Machine Learning for Beginners 1) Data Science for Beginners - A Curriculum By: Azure Cloud Advocates at Microsoft Stars ⭐️: 15K Fork: 2.4K Repo: https://microsoft.github.io/Data-Science-For-Beginners/#/?id=lessons 2) Machine Learning for Beginners - A Curriculum By: Azure Cloud Advocates at Microsoft Stars ⭐️: 38K Fork: 7.4K Repo: https://microsoft.github.io/ML-For-Beginners/#/

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Deep Learning free courses Introduction to Deep Learning 🎬 10 video lesson Duration ⏰: 1 week worth of material 🏃‍♂️ Self paced 📄 Notes, 👨‍🏫 Labs and many more ☢️ Projects, Competitions Teacher: Alexander Amini, Ava Soleimany Source: MIT 🔗 Course link Practical Deep Learning For Coders 🎬 8 video lessons 📔 Book Read online 📄 Notes, 👨‍🏫 Labs and many more Duration ⏰: 7 weeks long, 10 hours a week 🏃‍♂️ Self paced Teacher: Jeremy Howard Source: fast.ai 🔗 Course link Deep Learning by Kaggle, on youtube 🎬 13 video lesson Duration ⏰: 2 hours worth of material 🔗 Course link Learn Deep Learning and TensorFlow, without a Ph.D. 🎬 8 video lesson Duration ⏰: 3 hours worth of material 🏃‍♂️ Self paced 📄 Notes, slides Teacher: Martin Görner Source: Google Cloud 🔗 Course link Explore Deep Learning for Natural Language Processing 🎬 9 video lesson Duration ⏰: 7-8 hours worth of material 🏃‍♂️ Self paced Resource: Trailhead 🔗 Course link Deep Learning Summer School 🎬 35 video lesson Duration ⏰: 35+ hours 🏃‍♂️ Self paced Resource: deeplearning 🔗 Course link Deep Learning Prerequisites: The Numpy Stack in Python V2 Rating ⭐️: 4.5 out of 5 Students 👨‍🎓: 2230 Duration ⏰: 1hr 59min Created by Lazy Programmer Team, Lazy Programmer Inc. 🔗 Course link AI 101 Video Presentation presentation given by 👨‍🏫: MIT’s Brandon Leshchinskiy 🔗 Presentation link Deep Learning in Life Sciences - Spring 2021 🎬 22 video lesson Duration ⏰: 31 hours worth of material 🏃‍♂️ Self paced Teacher: Manolis Kellis Resource: Class Central 🔗 Course link Intro to Deep Learning by Kaggle Use TensorFlow and Keras to build and train neural networks for structured data. Duration ⏰: 4 hours 🔗 Course link Deep Learning An MIT Press book 📚 Authers: Ian Goodfellow, Yoshua Bengio and Aaron Courville 🔗 Book link #Deep_Learning #deeplearning #dl #machinelearning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

Reasons Why Data Goes Missing Understanding the reason for the missing data in your dataset is important because it helps you determine the type of missing data and what you need to do about it. Lets get our brain to grasp this concept shall we?😁😁 Missing Completely at Random(MCAR): This is a fact that a certain missing value has nothing to do with its hypothetical value and values of other variables. eg: You collect data on end-of-year holiday spending patterns. You survey adults on how much they spend annually on gifts for family and friends in dollar amounts. You note that there are a few missing values in your holiday spending dataset. Some people started answering your survey but dropped out or skipped a question. However, you note that you have data points from a wide distribution, ranging from low to high values. Therefore, you conclude that the missing values aren’t related to any specific holiday spending amount range. Missing at Random(MAR):This means that the propensity for a data point to be missing is unrelated to the missing data but related to some observed data. eg: You repeat your data collection with a new group. You notice that there are more missing values for adults aged 18–25 than for other age groups. But looking at the observed data for adults aged 18–25, you notice that the values are widely spread. It’s unlikely that the missing data are missing because of the specific values themselves. Instead, some younger adults may be less inclined to reveal their holiday spending amounts for unrelated reasons (e.g., more protective of their privacy). Missing Not at Random(MNAR): This is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). eg: If some participants with low incomes avoid reporting their holiday spending amounts because they are low in your datast, then this is a MNAR problem

microsoft/Data-Science-For-Beginners Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'. Creator: Microsoft Stars ⭐️: 11.1k Forked By: 1.9k GithubRepo: https://github.com/microsoft/Data-Science-For-Beginners ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Artificial Neural Networks (ANN) with Keras in Python and R Rating ⭐️: 4.7 out of 5 Duration ⏰: 11 hours on-demand video Students 👨‍🏫: 143,495 Created by: Start-Tech Academy 🔗 Course link Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments #ai #ml #neural_networks #machine_learning #data_science #deep_learning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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