資料科學從頭學（二）初學．Udemy 4門初學課程
（文章內容皆為記錄本人之學習過程，非以分享為目的）
一、目標：
7、8月完成 Udemy 4門資料科學相關課程，總時數 93.5小時。
二、做法：
7月份完成課程一遍，8月份挑出不熟與需要加強的地方精進。
7月份平均每周23.4小時，等於每天4.6小時(五天) + 2小時以上練習。
( google calendar 紀錄課程與累積時數)
三、原則：
每堂課邊看邊分類出 1.基礎:一定要懂、 2.八月份精進精進名單、3.有概念就好
四、課程內容：
Course1. Data Science A-Z：Real-Life Data Science Exercises (21 Hours)
This course will give you a full overview of the Data Science journey.
Upon completing this course you will know:
- How to clean and
prepare your data for analysis
- How to perform basic
visualisation of your data
- How to model your data
- How to curve-fit your
data
- And finally, how to
present your findings and wow the audience
In this course you will develop a good understanding of the following
tools:
- SQL
- SSIS
- Tableau
- Gretl
重點：
1) 學整體概念優先於個別工具學習
2) 掌握資料庫相關概念
注意：
1) Tableau、Gretl 都是付費工具，以體驗為主，不求精進
Course2. Machine Learning A-Z：Hands-on Python & R in Data Science(41 Hours)
Structured:
- Part 1 - Data
Preprocessing (與課程1重複)
- Part 2 - Regression:
Simple Linear Regression, Multiple Linear
Regression, Polynomial Regression, SVR, Decision Tree
Regression, Random Forest Regression
- Part 3 -
Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes,
Decision Tree Classification, Random Forest Classification
- Part 4 - Clustering:
K-Means, Hierarchical Clustering
- Part 5 - Association
Rule Learning: Apriori, Eclat
- Part 6 - Reinforcement
Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 - Natural
Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 - Deep Learning:
Artificial Neural Networks, Convolutional Neural Networks
- Part 9 - Dimensionality
Reduction: PCA, LDA, Kernel PCA
- Part 10 - Model
Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid
Search, XGBoost
重點：
1) 初步了解、並選擇幾個ML工具進入八月精進名單
2) Deep Learning 概念掌握
3) 學習老師的 Python 語法
注意：
1) 要另外花時間補一些統計與數學知識
2) R語言先瀏覽，但不刻意學習
Course3. Data Science and Machine Learning with Python - Hands On (9 Hours)
- Extract meaning from
large data sets using a wide variety of machine learning, data
mining, and data science techniques with the Python programming
language.
- Perform machine
learning on "big data" using Apache Spark and its MLLib
package.
- Design experiments and
interpret the results of A/B tests
- Visualize clustering and
regression analysis in Python using matplotlib
- Produce automated
recommendations of products or content with collaborative filtering
techniques
- Apply best
practices in cleaning and preparing your data prior to analysis
- Regression analysis
- K-Means Clustering
- Principal Component
Analysis
- Train/Test and cross
validation
- Bayesian Methods
- Decision Trees and
Random Forests
- Multivariate Regression
- Multi-Level Models
- Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- K-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency /
Inverse Document Frequency
- Experimental Design and
A/B Tests
重點：
1) 比較與課程2的講法差異，選擇幾個ML工具進入八月精進名單
2) Apache Spark 概念掌握
3) 學習老師的 Python 語法，與課程2比較
注意：
1) 要另外花時間補一些統計與數學知識
Course4. Deep Learning A-Z：Hands-on Artificial Neural Networks (22.5 Hours)
- Understand the
intuition behind Artificial Neural Networks
- Apply Artificial Neural
Networks in practice
- Understand the
intuition behind Convolutional Neural Networks
- Apply Convolutional
Neural Networks in practice
- Understand the
intuition behind Recurrent Neural Networks
- Apply Recurrent Neural
Networks in practice
- Understand the
intuition behind Self-Organizing Maps
- Apply Self-Organizing
Maps in practice
- Understand the
intuition behind Boltzmann Machines
- Apply Boltzmann
Machines in practice
- Understand the
intuition behind AutoEncoders
- Apply AutoEncoders in
practice
重點：
1) 掌握Artificial
Neural Networks概念與目前發展狀況
2) 先挑一個深入精進
快速概念參考:
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