Reading List for new comers to AI

A list of small bite sized articles for fundamentals in topics underpinning AI from the very basic

https://www.dataquest.io/blog/basic-statistics-with-python-descriptive-statistics/

https://blogs.oracle.com/ai/types-of-machine-learning-and-top-10-algorithms-everyone-should-know

https://www.kdnuggets.com/2018/05/5-reasons-logistic-regression-first-data-scientist.html
https://www.kdnuggets.com/2018/02/logistic-regression-concise-technical-overview.html

https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861

https://machinelearningmastery.com/a-gentle-introduction-to-the-central-limit-theorem-for-machine-learning/
https://heartbeat.fritz.ai/introduction-to-decision-tree-learning-cd604f85e236

https://medium.com/@ODSC/understanding-the-3-primary-types-of-gradient-descent-987590b2c36

• Linear regression - https://bit.ly/1LmnkPf
• Logistic regression - https://bit.ly/2jH2lhi
• SVM - https://bit.ly/2hM1Epx
• Random forest - https://bit.ly/1WtJjw4
• Gradient boosting - https://bit.ly/2AMNvyl
• PCA - https://bit.ly/1MkX8V3
• K-means clustering - https://bit.ly/1Pfa0B9
• Collaborative filtering - https://bit.ly/2Iqf6Ku
• kNN - https://bit.ly/2ryyhO4
• ARIMA - https://bit.ly/2iRiy55

Bonus: Neural networks - https://bit.ly/1I5qib7


course notes and book
➡ https://bit.ly/2IFr0n7
➡ https://lnkd.in/ebSzHPn