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Indie-Masters of Data Science for Business
An Introduction
Program Overview
How to navigate
Curriculum
Project Portfolio
Weekly Reflections
Learning Materials
General Notes
Extra Thoughts & Things
More
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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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