Skip to content

iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python

License

Notifications You must be signed in to change notification settings

erik1110/Data-Science

Repository files navigation

Machine-Learning

This is a story related to machine learning and all data science skill.

Keys of Machine Learning

  • Steps for building a Machine Learning

    1.Asking Questions

    2.Data Preparation(Preprocessing:Categorial feature,missing value and standardization;feature selection)

    3.Building Models with Different Hyperparameters

    4.Test and Evaluate Models

  • Machine Learning Models

    1.Objective function (loss function)

    2.Approximation function (to approximate the true function)

    3.Optimization Method (to optimize the objective function)

Machine Learning Roadmap

Name iThome 鐵人賽 Material & Assignment & Reference
Level 1 : Python Basic Skills Day 01:Python 介紹與開發環境
Day 02:Python 基礎觀念 (1)
Day 03:Python 基礎觀念 (2)
Day 04:Python 基礎觀念 (3)
A:01_H1_Basic
A:01_S1_Basic
R:Codecadedy Learn Python 3
Level 2 : Data Preprocessing Day 05:Pandas 操作 (1)
Day 06:Pandas 操作 (2)
A:02_H1_Pandas
A:02_S1_Pandas
R:numpy和pandas中 axis(軸)概念
R:Excel與Pandas之間的愛恨糾葛1
R:Excel與Pandas之間的愛恨糾葛2
R:Excel與Pandas之間的愛恨糾葛3
R:Numpy & Pandas 簡介
Level 3 : Data Visualizing Day 07:Matplotlib 操作
Day 08:Seaborn 操作
A:03_H1_Visualizing
A:03_S1_Visualizing
Level 4 : Introduction of Tools R:[Day02]Jupyter Notebook操作
R:Jupyter Notebook介紹及安裝
R:JupyterLab
R:Vscode
R:Hackmd 常用 LaTeX
Level 5 : Database Related Day 09:資料庫介紹
Day 10:Postgres 操作
Day 11:psycopg2 操作
M:05_M2_Postgres_and_psycopg2
Level 6 : Python Advanced Skills Day 12:物件導向
Day 13:程式除錯與異常
Day 14:程式碼日誌與品質
M:06_M1_Object-Oriented-Programming
M:06_M2_Error_and_Exception
M:06_M3_Clean_Code
M:06_M4_Decorator
Level 7 : Model Prerequisite Knowledge Day 15:機器學習介紹
Day 16:模型衡量指標
Day 17:資料預處理 (1)
Day 18:資料預處理 (2)
M:07_M1_Model_Prerequisite_Knowledge
M:07_M2_Data_Preprocessing
R:[Day24]什麼是機器學習?
R:李宏毅教授的影片
Introduction of Machine Learning
Regression-Case Study
Regression-Demo
What does the error come from?
Gradient Descent
Classification
Logistic Regression
Level 8 : Model Development Day 19:KNN 與 K-means
Day 20:線性迴歸與羅吉斯迴歸
Day 21:SVM
Day 22:決策樹
Day 23:集成式學習
Day 24:隨機森林
Day 25:XGBoost
Day 26:LightGBM 與 GridSearch
Day 27:模型解釋 Shap
M:08_M0_Background_Knowledge
M:08_M1_KNN&K-Means
M:08_M2_Regression
M:08_M3_SVM
M:08_M4_Decision_Tree
M:08_M5_Random_Forest
M:08_M6_XgBoost
M:08_M7_LightGBM
M:08_M8_GridSearch
M:08_M9_Shap
Level 9 : Git Tutorial Day 28:Git M:09_M1_Git
Level 10 : API Service Day 29:FastAPI 讓模型上線 M:10_API_Service
Level 11 : Model Monitoring

Advanced

Name Material & Assignment & Reference
Level 12 : Deep Learning M:TF-IDF使用
M:nltk
M:What's cooking
M:Keras PDF
M:Keras Demo
R:卷積神經網絡介紹(Convolutional Neural Network)
R:關於影像辨識,所有你應該知道的深度學習模型
R:OCR技术:大批量构造中文文字训练集
R:Google Cloud Vision API
R:Google Cloud Text API