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Indie-Masters of Data Science for Business

An Introduction

Program Overview

Extra Thoughts & Things

Explore

Extra Thoughts & Things

This is just an ongoing dump of info, links, resources or other things that I spot that may or may not be useful. No order. Sometimes no sense.

Machine Learning Algorithms

Linear Regression (

Georgia Tech⁠

StatQuest⁠

)

Logistic Regression (

StatQuest⁠

)

K-Nearest Neighbours (

MIT⁠

)

Decision Trees (

StatQuest⁠

)

Naive Bayes (

Terence Shin⁠

Luis Serrano⁠

)

Support Vector Machines (

SVM Tutorial⁠

Alice Zhao⁠

)

Neural Networks (

Terence Shin⁠

)

Random Forests (

StatQuest⁠

)

AdaBoost (

Terence Shin⁠

StatQuest⁠

)

Gradient Boost (

StatQuest⁠

)

XGBoost (

StatQuest⁠

)

Principal Component Analysis (

StatQuest⁠

)

Machine Learning Concepts

⁠

Bia and Variance Tradeoff⁠

⁠

Regularization⁠

⁠

Confusion matrix and relevant metrics⁠

⁠

AUC and ROC (video)⁠

⁠

Bootstrap Sampling⁠

⁠

Ensemble Learning, Bagging, and Boosting⁠

⁠

Normalization vs Standardization⁠

⁠

Possible Projects worth considering

⁠

Calories Burned Prediction⁠

⁠

Stock Price Prediction⁠

⁠

Data Science Project on Area and Population⁠

⁠

Data Science Project on Time Series⁠

⁠

Data Science Project on Birth Rate Analysis⁠

⁠

Data Science Project on President Heights⁠

⁠

Advanced Projects:

https://thecleverprogrammer.com/2020/11/15/machine-learning-projects/⁠

⁠

https://www.hackerrank.com/dashboard⁠

⁠

https://leetcode.com/problemset/database/⁠

⁠

Project Inspiration

Predicting Wine Quality w/ Classification Techniques (

Article⁠

Github⁠

)

Coronavirus Data Visualizations using Plotly (

Article⁠

Github⁠

)

Collaborative Filtering Recommendation System for Movies (

Github⁠

)

Here’s

a list of data science projects⁠

that you can look at to generate ideas and come up with an interesting side project of your own. (Thanks

Terence Shin⁠

)

⁠

https://www.w3schools.com/sql⁠

⁠

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