Here's 7 statistical & machine learning concepts that you should know for #DataScience:

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1. Tree Based Methods
a. Decision Trees - https://lnkd.in/gBtCk9G
b. Random Forest - https://lnkd.in/g9FkczK
c. Gradient Boosting Trees - https://lnkd.in/gFPFGsk

2. Linear (Regularized) Models
a. Lasso & Ridge - https://lnkd.in/g3dJT-g
b. Linear Regression - https://lnkd.in/g7AS6Ar
c. Logistic Regression - https://lnkd.in/gq4EyJc

3. Hypothesis Testing & confidence
a. A/B Testing - https://lnkd.in/gmeijHV
b. Chi Square Test - https://lnkd.in/gG6vz2T
c. Statistical Tests - https://lnkd.in/gJcfTsq

4. Resampling Methods
a. Bootstrapping and Bagging - https://lnkd.in/gPmm4by
b. Cross Validation - https://lnkd.in/gsfsE6y

5. Clustering K-means
https://lnkd.in/gvNsp8N

6. Feature Selection
https://lnkd.in/gdCBWpB

7. Evaluation Metrics
a. Classification Metrics - https://lnkd.in/gxeyC6n
b. Regression Metrics - https://lnkd.in/gj4Eg9p

Harvard CS109A #DataScience course materials — huge collection free & open!

1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html


It will be really useful for you


invite your friends 🌹🌹
@Deeplearning_ai