關聯博客：機器學習與深度學習相關紙質資源及介紹 入門機器學習
一、必學篇
1. Coursera-Stanford：Machine Learning_NG
這一部分早期我寫過幾周的博客，大家可以參考~
一共11周課程，每一週視頻時長大約1-3小時，每週作業平均需要花費3小時左右。
建議課本：統計學習方法+西瓜書+機器學習實戰
2. Specialization-Stanford：Deep Learning_NG
相關blog專欄：coursera_deep_learning
一共是5部分的課程，每一個部分是3周左右的視頻內容（每一週大約需要3-5小時）
建議課本：深度學習+實戰書（Tensorflow實戰）
Neural Networks and Deep Learning：4 weeks
[coursera/dl&nn/week1]Introduction to deep learning(summary&question)
[coursera/dl&nn/week2]Basics of Neural Network programming(2.1 Logistic Regression as a NN)
[coursera/dl&nn/week2]Basics of Neural Network programming(2.2 py & Vectorization)
[coursera/dl&nn/week2]Basics of Neural Network programming(quiz)
[coursera/dl&nn/week3]Shallow Neural Network(summary&question)
[coursera/dl&nn/week4]Deep Neural Network(summary&question)
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization： 3 weeks
[coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
[coursera/ImprovingDL/week2]Optimization algorithms(summary&question)
[coursera/ImprovingDL/week3]Hyperparameter tuning, Batch Normalization(summary&question)
Structuring Machine Learning Projects：2 weeks
[coursera/StructuringMLProjects/week1&2]ML Strategy1(summary&question)
[coursera/StructuringMLProjects/week1&2]ML Strategy2(summary&question)
Convolutional Neural Networks：4 weeks
[coursera/ConvolutionalNeuralNetworks/week1]Foundations of cnn(summary&question)
[coursera/ConvolutionalNeuralNetworks/week2]Deep CNN Models: case studies(summary&question)
[coursera/ConvolutionalNeuralNetworks/week3]Object Detection(summary&question)
[coursera/ConvolutionalNeuralNetworks/week4]Face recognition & Neural (summary&question)
Sequence Models：3 weeks
[coursera/SequenceModels/week1]Recurrent Neural Networks (summary&question)
[coursera/SequenceModels/week1]Character level language model - Dinosaurus land[assignment]
[coursera/SequenceModels/week1]Improvise a Jazz Solo with an LSTM Network - v1[assignment]
[coursera/SequenceModels/week2]Operations on word vectors - Debiasing[assignment]
[coursera/SequenceModels/week2]Emojify![assignment]
[coursera/SequenceModels/week3]Sequence models & Attention mechanism (summary&question)
[coursera/SequenceModels/week3]Neural machine translation with attention[assignment]
[coursera/SequenceModels/week3]Trigger Word Detection[assignment]
二、機器學習補充篇
1. Specialization-UW：Machine Learning Specialization
UW的專項比較基礎，專業一共是4個課程，首先介紹一個實例，然後從迴歸、分類、聚類三個角度的三個課程
Machine Learning Foundations: A Case Study Approach
Machine Learning: Regression
Machine Learning: Classification
Machine Learning: Clustering & Retrieval
2. Specialization-UMich：Applied Data Science with Python Specialization
UMich的課程主要是以實戰爲主，一共是4個課程。
Introduction to Data Science in Python
Applied Plotting, Charting & Data Representation in Python
Applied Machine Learning in Python
Applied Text Mining in Python
3. Specialization-JHU：Data Science Specialization
JHU的課程比較多，一共10個課程，總體來說聽下來還是很不錯的。
The Data Scientist’s Toolbox
R Programming
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Developing Data Products
Data Science Capstone
4. Specialization-Google：Machine Learning with TensorFlow on Google Cloud Platform Specialization
Google發佈的ML課程，在TF平臺上實現ML，一共5個課程，主要是針對項目。
How Google does Machine Learning
Launching into Machine Learning
Intro to TensorFlow
Feature Engineering
Art and Science of Machine Learning
5. Specialization-ICL：Mathematics for Machine Learning Specialization
帝國理工開的數學前導課，一共三個課程。
Mathematics for Machine Learning: Linear Algebra
Mathematics for Machine Learning: Multivariate Calculus
Mathematics for Machine Learning: PCA
課本參考深度學習第一部分
6. Specialization-UCSD：Big Data Specialization
Introduction to Big Data
Big Data Modeling and Management Systems
Big Data Integration and Processing
Machine Learning With Big Data
Graph Analytics for Big Data
Big Data - Capstone Project
三、深度學習補充篇
1. Specialization-RUS(RUSSIA)：Advanced Machine Learning Specialization
俄羅斯高等經濟學院的課程，一共是7個課程。
Introduction to Deep Learning
How to Win a Data Science Competition: Learn from Top Kagglers
Bayesian Methods for Machine Learning
Practical Reinforcement Learning
Practical Reinforcement Learning
Deep Learning in Computer Vision
Natural Language Processing
Addressing Large Hadron Collider Challenges by Machine Learning
2. Specialization-IBM：Advanced Machine Learning Specialization
一共4個課程。
Fundamentals of Scalable Data Science
Advanced Machine Learning and Signal Processing
Applied AI with DeepLearning
Advanced Data Science Capstone
四、延伸課程
1. Specialization-Illinois：雲計算 Specialization
一共6個課程。
Cloud Computing Concepts, Part 1
雲計算基礎：第 2 部分
Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure
Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud
雲端計算
Cloud Computing Project
2. Specialization-Duke：Statistics with R Specialization
一共5個課程。
Introduction to Probability and Data
Inferential Statistics
Linear Regression and Modeling
Bayesian Statistics
Statistics with R Capstone
3. NYUTandon：Overview of Advanced Methods of Reinforcement Learning in Finance