R Programming Live - Lecture 4 | Kaggle Notebook + 6 Machine Learning Methods

TIMESTAMPS
00:00 Predictive analytics
00:45 Supervised/ Unsupervised learning
04:15 Model development/ deployment
06:30 Kaggle
08:35 Kaggle R notebook
12:15 Data
17:45 Decision tree
24:25 Random forest
31:00 Naive Bayes
32:35 Support vector machines
35:20 Logistic regression
37:20 Neural networks (typo: 2nd last code should have p6 greater than 0.5)

[aashijain]

"The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires."
Professor, you are great. Thank you so much for your informative classes.

[MemphianSounds]

Incredible - as always. Would you take the results of each model's initial CM run to direct you towards which model to focus on tuning? - or do you think with tuning the ranking of the quality of the models is likely to change?

[harunbakirci]

Thank you so much Sir I have learnt a lot of things from you thank you so much 👌👌👌👌👌👏👏👏👏👏👏👏

[sergiomorellmonzo]

Very interesting class, but I have a question. Using the Kaggle platform you are computing in local or in cloud? If it is the second case, how can I save my model fitted and download?

[salvationarmy]

Hi, i didnt understand why for the neural net p6 = ifelse(p6 < 0.5, 1, 0) while the others were > 0.5.
Can you please explain sir? Thank you

[sidraghayas]

I want your class on distribution identification, box Cox transformation and Monte Carlo simulation