[机器学习入门课程]机器学习与深度学习相关课程资源及介绍

关联博客：机器学习与深度学习相关纸质资源及介绍  入门机器学习

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一、必学篇

1. Coursera-Stanford：Machine Learning_NG

这一部分早期我写过几周的博客，大家可以参考~

一共11周课程，每一周视频时长大约1-3小时，每周作业平均需要花费3小时左右。

建议课本：统计学习方法+西瓜书+机器学习实战

 

2. Specialization-Stanford：Deep Learning_NG

相关blog专栏：coursera_deep_learning

一共是5部分的课程，每一个部分是3周左右的视频内容（每一周大约需要3-5小时）

建议课本：深度学习+实战书（Tensorflow实战）

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]coding

[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、机器学习补充篇

1. Specialization-UW：Machine Learning Specialization

UW的专项比较基础，专业一共是4个课程，首先介绍一个实例，然后从回归、分类、聚类三个角度的三个课程

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的课程主要是以实战为主，一共是4个课程。

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的课程比较多，一共10个课程，总体来说听下来还是很不错的。

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发布的ML课程，在TF平台上实现ML，一共5个课程，主要是针对项目。

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

帝国理工开的数学前导课，一共三个课程。

Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Multivariate Calculus

Mathematics for Machine Learning: PCA

课本参考深度学习第一部分

 

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、深度学习补充篇

1. Specialization-RUS(RUSSIA)：Advanced Machine Learning Specialization

俄罗斯高等经济学院的课程，一共是7个课程。

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个课程。

Fundamentals of Scalable Data Science

Advanced Machine Learning and Signal Processing

Applied AI with DeepLearning

Advanced Data Science Capstone

 

 

四、延伸课程

1. Specialization-Illinois：云计算 Specialization

一共6个课程。

Cloud Computing Concepts, Part 1

云计算基础：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

云端计算

Cloud Computing Project

 

2. Specialization-Duke：Statistics with R Specialization

一共5个课程。

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