LinkedIn-Daily-Posts

KO | EN

gitlite — search

#javascript #python #react #typescript #go #golang #machine-learning #rust #docker #nodejs #awesome #awesome-list #security #open-source

LinkedIn-Daily-Posts

(★ 33)

This is a curated list of all my posts on LinkedIn regarding Data Science, which I publish daily.

LinkedIn-Daily-Posts Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# LinkedIn-Daily-Posts
This is a curated list of all my posts on LinkedIn regarding Data Science, which I publish daily. Hit the star if you like my work!
--------------
--------------
##### List Updated with all 100 posts :)
--------------
--------------
## > Day 0: Introducing the Series
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-ml-data-activity-6684428395431936000-YrK3
-------
## > Day 1: Optimization
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-ml-activity-6684763368823304193-yyQf
-------
## > Day 2: OLS (Ordinary Least Squares)
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-ml-activity-6685103630770593792-Dwxu
-------
## > Day 3: Gradient Descent
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-ml-activity-6685498835516620800-vfBH
-------
### > Day 4: Stochastic & Batch Gradient Descent
Link: https://www.linkedin.com/posts/chayankathuria_datascience-ml-machinelearning-activity-6685842819376050176-msfg
-------
### > Day 5: Probability Distributions
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-ml-activity-6686233389558743040-ICcV
-------
### > Day 6: Normal Distribution
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6686565932434808832-hQB8
-------
### > Day 7: Central Limit Theorem
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6686941377974079488-OxSp
-------
### > Day 8: Hypothesis Testing
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6687321402288209920-Tthh
-------
### > Day 9: Significance Level & Confidence Interval
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6687689231625728000-_1E6
-------
### > Day 10: P-Value
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6688024995152625664-o6H6
-------
### > Day 11: Z-Tests
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6688403672336019456-91-K
-------
### > Day 12: Skewness
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6688740361910697984-3l41
-------
### > Day 13: Kurtosis
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6689114213031448576-XjJv
-------
### > Day 14: Correlation and Covariance
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6689466545426497538-lbQM
-------
### > Day 15: Quartiles and Box Plots
Link: https://www.linkedin.com/posts/chayankathuria_datascience-statistics-machinelearning-activity-6689832018328510464-x0n5
-------
### > Day 16: Probability VS Likelihood
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-activity-6690199294105976833--dPO
-------
### > Day 17: Statistical Power
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6690540669615820800-zc_P
-------
### > Day 18: P-Hacking
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-activity-6690923291785400320-3Ble
-------
### > Day 19: Power Analysis
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-activity-6691276299366920192-MC2b
-------
### > Day 20: Bar Charts Vs. Pie Charts
Link: https://www.linkedin.com/posts/chayankathuria_datascience-datavisualization-machinelearning-activity-6691622506308136960-ud3a
-------
### > Day 21: Standard Deviation Vs Standard Error
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6692014438750466048-cLwj
-------
### > Day 22: Probability Basics💡
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6692358844871450624-r9Jx
-------
### > Day 23: Naive Bayes
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-machinelearning-activity-6692739578773729280-ZO5j
-------
### > Day 24: Bias-Variance Trade-off💡
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6693114813154938880-pRiS
-------
### > Day 25: Vectorization & Broadcasting in Python
Link: https://www.linkedin.com/posts/chayankathuria_datascience-python-activity-6693448730978369536-HZC6
-------
### > Day 26: Why Mean Squared Error?
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6693812529426169856-uygR
-------
### > Day 27: Data Science Project Lifecycle💡
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6694160151341002752-5gxe
-------
### > Day 28: Lazy Evaluation vs Eager Evaluation
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6694518816854089728-PEiW
-------
### > Day 29: What are Tensors?💡
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-tensorflow-activity-6694909148674502656-XSzn
-------
### > Day 30: Hummingbird by Microsoft🦜
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6695293386959712256-_N8o
-------
### > Day 31: Data Collection🗃️🔐
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-data-activity-6695627090273857536-xQrm
-------
### > Day 32: Pandarallel📊
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-pandas-activity-6695974919941042176-aJdc
-------
### > Day 33: Databases🗃️
Link: https://www.linkedin.com/posts/chayankathuria_datascience-data-database-activity-6696354746384596992-3WN5
-------
### > Day 34: Exploratory Data Analysis📊
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelarning-activity-6696705042646396928-Fsha
-------
### > Day 35: Assumptions of Linear Regression
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6697058984727244800-zH2G
-------
### > Day 36: Multicollinearity
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6697427886493827072-pX4P
-------
### > Day 37: Heteroscedasticity 
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-statistics-activity-6697813832045260800-vthH
-------
### > Day 38: Auto EDA
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6698172053373153280-UNu_
-------
### > Day 39: *args and **kwargs in Python
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6698516519900524544-5YA4
-------
### > Day 40: Data Preprocessing
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6698877321664892928-YLwN
-------
### > Day 41: QuickDA⚡
Link: https://www.linkedin.com/posts/chayankathuria_datascience-python-machinelearning-activity-6699258527657074688-DKIc
-------
### > Day 42: Feature Engineering ⚙️🔧
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6699616146213232640-aypj
-------
### > Day 43: Why is Sample Variance divided by N-1?
Link: https://www.linkedin.com/posts/chayankathuria_statistics-datascience-activity-6699974384473530368-UKmV
-------
### > Day 44: Feature Scaling
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6700339912673157120-bXM3
-------
### > Day 45: Inconsistencies in Data
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6700682723532558336-44T3
-------
### > Day 46: Zipping up in Python 🤐
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6701050979535634433-cFMZ
-------
### > Day 47: Handling Outliers
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6701407026502791168-Mz-k
-------
### > Day 48: Bamboolib🎍
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6701767896818868224-6y6K
-------
### > Day 49: Categorical Variables Encoding
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6702138026950213632-mW7H
-------
### > Day 50: Evaluation metrics
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6702479977608228864-8bMt
-------
### > Day 51: Precision vs Recall vs F1
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6702858273470627840--TWc
-------
### > Day 52: Is F1 Score good enough?
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6703204220276936704-X4kh
-------
### > Day 53: What exactly is AUC ROC?🤔
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6703569196304994304-_k73
-------
### > Day 54: What's Kappa Score?🙄
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6703951753248854016-ceYh
-------
### > Day 55: Micro & Macro Averaged Precision
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6704304180292571136-lQb6
-------
### > Day 56: Precison @ K
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6704706360002568192-PA51
-------
### > Day 57: The ML Flow UI
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6705016957927780352-iiaN
-------
### > Day 58: 3 Smartest Python Functions
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6705428241882120192-1dAd
-------
### > Day 60: Obviously AI
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6705750593878732800-yViP
-------
### > Day 61: R Squared Vs Adjusted R Squared
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6706131228317372416-p8p_
-------
### > Day 62: What is Predicted R Square?💡
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6706473991286140928-aAlv
-------
### > Day 63: STOP imputing missing values with the MEAN !
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-deeplearning-activity-6706809956001206272-dxWf
-------
### > Day 64: How to handle missing values in Test Data
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6707190817309691904-kbCI
------
### > Day 65: What are Python Decorators?
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6707584957142884352-Oul0
------
### > Day 66: Binning with Pandas📊🐼
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6707916032620720128-G-IJ
------
### > Day 67: Logistic Regression is linear or non-linear?🤔
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-activity-6708308645018447872-ghG6
------
### > Day 68: A faster way to iterate through DataFrames⚡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6708675592541270016-XVnA
------
### > Day 69: Shallow Copy Vs Deep Copy in Python
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6708998987317358592-YrYy
------
### > Day 70: Are tuples really immutable?💡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6709384535534034944-wlDP
------
### > Day 71: What are Namespaces in Python?🤔
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6709727171503550464-lCMH
------
### > Day 72: Iterators Vs Generators in Python⚡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6710091160481796096-XCKx
------
### > Day 73: What is difference between == and is ?⚡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6710460794401820673-qeQ0
------
### > Day 74: List and Dictionary Comprehension in Python💡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6710830017472978944-ZOpr
------
### > Day 75: Python statement I hated the most🤯
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6711178540542418944-oblh
------
### > Day 76: Are you reading/writing files the right way❓
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6711550161472028672-vAFZ
------
### > Day 77: Is your Machine Learning model actually learning ?🤔
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6712266684461305856-JciZ
------
### > Day 78: Have you ever used this Python module?⚡
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6712633995684851712-PW_R
------
### > Day 79: 3 Sweet Python One Liners
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6713004353134891008-LRyg
------
### > Day 80: 3 Statistical concepts you must know
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-statistics-activity-6713360517051797504-ipIM
------
### > Day 81: 3 functions that will make you better at NumPy
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6713724148411092992-xxan
------
### > Day 82: 3 AutoML libraries that you should know🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6714091582078955520-Iox_
------
### > Day 83: 3 Pandas functions I wish I'd known earlier
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-python-activity-6714472715455131648-RvUe
------
### > Day 84: Will AutoML replace Data Scientists?🤔
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-ai-activity-6714807621464977408-9ULV
------
### > Day 85: Think Data Visualization? Think Plotly!🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6715193481385578496-lGth
------
### > Day 86: The fastest way to create data apps⚡🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6715564223465873408-0a11
------
### > Day 87: Say hello to the all new Python 3.9!🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6715904578606415872-0hyl
------
### > Day 88: Use your NumPy on Steroids
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6716273038586904576-cOGb
------
### > Day 89:  3 Practices that will make you better at Python🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6716620486027177984-bxsJ
------
### > Day 90: The smarter way of exploring missing data🤞
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6717002439372939265-hqdw
------
### > Day 91: Difference between Pandas Object & String dtype 🐼
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6717350437953048576-LDB1
------
### > Day 92: 3 String operations you should definitely know👇
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-machinelearning-activity-6717727621683175424-ghcy
------
### > Day 93: Dask for Big Data
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6718069739278651392-FUmm
------
### > Day 94: Level up your ML game with this tool!🚀
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6718450402733408256-_OgN
------
### > Day 95: 3 Sklearn functions you should know💡
Link: https://www.linkedin.com/posts/chayankathuria_python-machinelearning-datascience-activity-6718804202753204224-2dux
------
### > Day 96: A Machine Learning project template📝
Link: https://www.linkedin.com/posts/chayankathuria_datascience-machinelearning-activity-6719165172717535232-JiXf
------
### > Day 97: 6 Git commands you should know!
Link: https://www.linkedin.com/posts/chayankathuria_git-datascience-python-activity-6719533086905815040-MRoK
------
### > Day 98: What does explainability in ML mean?🤔
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-python-activity-6719891143011700736-qn3q
------
### > Day 99: Python meets Marvel💥
Link: https://www.linkedin.com/posts/chayankathuria_python-datascience-activity-6720253481749401600-kbvy
------
### > Day 💯!: What are Docker and Kubernetes?🚀
Link: https://www.linkedin.com/posts/chayankathuria_machinelearning-datascience-ai-activity-6720602926047399936-04av
------

LinkedIn-Daily-Posts

LinkedIn-Daily-Posts Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

LinkedIn-Daily-Posts

LinkedIn-Daily-Posts Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README