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哈佛“数据科学导论”课程对所有人免费开放!包括机器学习和回归分析等各种方法!

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接着“耶鲁开设“应用实证方法”P.hd课程, 强逻辑, 好文献, 重实操, 真前沿, 送slides和笔记!” 和“加拿大经济学会主席的"机器学习"课程可以学习了! 共计20份Slides直指ML前沿!” 今天,我们推荐一门绝对实在的哈佛《数据科学导论》课程,里面包括机器学习和回归分析等各种方法,而且该课程对所有人免费开放!课程材料包括讲义和软件实操代码,真的是非常容易入门又经典的课程。文后附上了课程主页和二维码,可以直接进入学习相关章节。
  1. 讲义

  2. R 代码、Python Notebooks

  3. 实验室材料

  4. 高级部分

欢迎来到《数据科学导论课程》。本课程是为期一年的数据科学导论的前半部分。该课程的重点是分析混乱的现实生活数据,使用统计和机器学习方法进行预测。
课程分为3个模块,每个模块都将使用数据开展如下五个关键方面的学习:
  1. 数据采集——数据整理、清洗、采样,得到合适的数据集;

  2. 数据管理——快速可靠地访问数据;

  3. 探索性数据分析——产生假设和建立直觉;

  4. 预测或统计学习;

  5. 交流——通过可视化、故事和可解释的总结来总结结果。

课程讲义


  • Lecture 1: Introduction (Sep. 03, 2019)

  • Lecture 2: Data and Data Exploration (Sep. 04, 2019)

  • Lecture 3: Pandas and Web Scraping (Sep. 11, 2019)

  • Lecture 4: Introduction to Regression (Sep. 16, 2019)

  • Lecture 5: Linear Regression (Sep. 18, 2019)

  • Lecture 6: Multiple Linear Regression, Polynomial Regression (Sep. 23, 2019)

  • Lecture 7: Model Selection and Regularization (Sep. 25, 2019)

  • Lecture 8: Regularization and EDA (Sep. 30, 2019)

  • Lecture 9: Visualization for Communication (Oct. 02, 2019)

  • Lecture 10: Logistic Regression (Oct. 07, 2019)

  • Lecture 11: Logistic Regression 2 (Oct. 09, 2019)

  • Lecture 12: KNN Classification & Imputation (Oct. 16, 2019)

  • Lecture 14: PCA (Oct. 23, 2019)

  • Lecture 15: Decision Trees (Oct. 28, 2019)

  • Lecture 16: Bagging, & Random Forest (Oct. 30, 2019)

  • Lecture 17: Boosting Methods (Nov. 04, 2019)

  • Lecture 18: Neural Networks 1 – Perceptron and MLP (Nov. 06, 2019)

  • Lecture 19: NN 2: Anatomy of NN, design choices (Nov. 11, 2019)

  • Lecture 20: NN 3: Back Propagation (Nov. 13, 2019)

  • Lecture 21: NN 4: Regularization methods (Nov. 18, 2019)

  • Lecture 22: Visualization for Model Interpretation (Nov. 20, 2019)

  • Lecture 23: Experimental Design & Testing I (Nov. 25, 2019)

  • Lecture 24: Experimental Design & Testing II (Dec. 02, 2019)

主题和R、Python代码实操

Activation Function

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

AdaBoost

  • Lecture 17: Boosting Methods

  • Lecture 17: Boosting Methods [Notebook]

Adaboost And Xgboost

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

Array Reshape

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

Bagging

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

  • S-Section 06: Bagging and Random Forest [Notebook]

  • S-Section 07: Bagging and Random Forest

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

  • Lecture 16: Bagging, & Random Forest

Batching

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

Bayesian

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Beautiful Soup

  • Lab 2: Pandas and Scraping

Beautifulsoup

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping

Bias

  • Lecture 8: Regularization and EDA

Biases

  • Lecture 8: Regularization and EDA

Big Data

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

Boosting

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

  • Lecture 17: Boosting Methods

  • Lecture 17: Boosting Methods [Notebook]

Bootstrap

  • Lecture 5: Linear Regression

Boundaries

  • Lecture 10: Logistic Regression [Notebook]

Categorical Predictors

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Categorical Variables

  • S-Section 03: Multiple Linear and Polynomial Regression [Notebook]

  • S-Section 03: Multiple Linear and Polynomial Regression

CI

  • Lecture 5: Linear Regression

Classification

  • Lecture 15: Decision Trees

  • Lecture 15: Decision Trees [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

  • Lecture 12: KNN Classification & Imputation

  • Lecture 10: Logistic Regression [Notebook]

Collinearity

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Communication

  • Lecture 9: Visualization for Communication

Confidence Intervals

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

  • Lecture 5: Linear Regression

Confusion Matrix

  • Lecture 11: Logistic Regression 2 [Notebook]

Crawl

  • Lab 2: Pandas and Scraping

Cross-Validation

  • Lecture 11: Logistic Regression 2 [Notebook]

  • S-Section 04: Regularization and Model Selection [Notebook]

  • S-Section 04: Regularization and Model Selection

  • Lab 4: Multiple and Polynomial Regression

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lecture 7: Model Selection and Regularization

CV

  • Lecture 11: Logistic Regression 2 [Notebook]

Data

  • Lecture 2: Data and Data Exploration

Data Cleaning

  • Lecture 3: Code Pandas + Beautiful Soup [Notebook]

Data Exploration

  • Lecture 2: Data and Data Exploration

Data Science Demo

  • Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]

  • Lecture 1: Data Science Demo [Notebook]

Data Science Process

  • Lecture 1: Introduction

Data Scraping

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping

Dataframe

  • Lecture 3: Pandas and Web Scraping

Decision Boundaries

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

Decision Trees

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

  • S-Section 06: Bagging and Random Forest [Notebook]

  • S-Section 07: Bagging and Random Forest

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

  • Lecture 15: Decision Trees

  • Lecture 15: Decision Trees [Notebook]

Demo

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Descriptive Statistics

  • Lecture 2: Data and Data Exploration

Dictionaries

  • Lab 1: Python basics, YAML environments, Numpy

Dimensionality Reduction

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

  • Lab 8: PCA

  • Lab 8: PCA [Notebook]

  • Advanced Sections 4: PCA

  • Lecture 14: PCA

  • Lecture 14: PCA [Notebook]

Dropout

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

Eda

  • Lecture 9: Visualization for Communication

  • Lecture 8: Regularization and EDA

  • Lecture 3: Pandas and Web Scraping

Eigenvalues

  • Advanced Sections 4: PCA

Eigenvectors

  • Advanced Sections 4: PCA

  • Advanced Section 1: Linear Algebra and Hypothesis Testing

Eignevalues

  • Advanced Section 1: Linear Algebra and Hypothesis Testing

Elastic Net

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Entropy

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

  • Lecture 15: Decision Trees

  • Lecture 15: Decision Trees [Notebook]

Explained Variance

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

Exploratory Data Analysis

  • Lecture 3: Pandas and Web Scraping

Feed Forward

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

Feed Forward Neural Networks

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

Functions

  • Lab 1: Python basics, YAML environments, Numpy

Gini Index

  • Lecture 15: Decision Trees

  • Lecture 15: Decision Trees [Notebook]

GLM

  • Advanced Section 3: Generalized Linear Models

  • Advanced Section 3: Generalized Linear Models [Notebook]

Google Sites

  • Lab 13: Making websites! [Notebook]

Gradient Descent

  • Lecture 17: Boosting Methods

  • Lecture 17: Boosting Methods [Notebook]

Html

  • Lab 13: Making websites! [Notebook]

Http

  • Lab 13: Making websites! [Notebook]

Hypothesis Testing

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

  • Advanced Section 1: Linear Algebra and Hypothesis Testing

  • Lecture 5: Linear Regression

Imputation

  • Lecture 12: KNN Classification & Imputation

Information Gain

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

Interaction Terms

  • S-Section 03: Multiple Linear and Polynomial Regression [Notebook]

  • S-Section 03: Multiple Linear and Polynomial Regression

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Introduction

  • Lecture 1: Introduction

K-Nearest Neighbors (KNN) Regression

  • Lab 3: Scikit-learn for Regression [Notebook]

Keras

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

KNN

  • Lecture 12: KNN Classification & Imputation

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

KNN-Classification

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

KNN Imputation Classification

  • Lecture 12: KNN Classification & Imputation [Notebook]

Knn K-Nearest Neighbors (KNN)

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

KNN Regression

  • Lecture 4: Introduction to Regression

Lasso

  • S-Section 04: Regularization and Model Selection [Notebook]

  • S-Section 04: Regularization and Model Selection

  • Lecture 8: Regularization and EDA

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Linear Algebra

  • Advanced Section 1: Linear Algebra and Hypothesis Testing

Linear Regression

  • Lab 6: Logistic Regression

  • Lab 6: Logistic Regression [Notebook]

  • Lab 6: Logistic Regression [Notebook]

  • Lab 4: Multiple and Polynomial Regression

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

  • Lecture 5: Linear Regression

  • Lab 3: Scikit-learn for Regression [Notebook]

Lists

  • Lab 1: Python basics, YAML environments, Numpy

Logistic Regression

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

  • Lab 6: Logistic Regression

  • Lab 6: Logistic Regression [Notebook]

  • Lab 6: Logistic Regression [Notebook]

  • Lecture 11: Logistic Regression 2 [Notebook]

  • Lecture 10: Logistic Regression [Notebook]

Logistics

  • Lecture 1: Introduction

Matplotlib

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

Metrics

  • Lecture 11: Logistic Regression 2 [Notebook]

Mle

  • Lab 6: Logistic Regression

  • Lab 6: Logistic Regression [Notebook]

  • Lab 6: Logistic Regression [Notebook]

MNIST

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

Model Selection

  • S-Section 04: Regularization and Model Selection [Notebook]

  • S-Section 04: Regularization and Model Selection

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

  • Lecture 7: Model Selection and Regularization

Multiclass

  • Lecture 11: Logistic Regression 2 [Notebook]

Multilayer Perceptron

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

Multinomial Regression

  • Lab 4: Multiple and Polynomial Regression

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lab 04: Multiple and Polynomial Regression [Notebook]

Multiple Linear Regression

  • S-Section 03: Multiple Linear and Polynomial Regression [Notebook]

  • S-Section 03: Multiple Linear and Polynomial Regression

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Multiple Logistic Regression

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

Neural Networks

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

NumPy

  • Lab 1: Python basics, YAML environments, Numpy

  • Lab 01: YAML Environments, Python basics, Numpy [Notebook]

OOB

  • Lecture 16: Bagging, & Random Forest

Out Of Bag Error

  • Lecture 16: Bagging, & Random Forest

Overfitting

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

P-Values

  • Lecture 5: Linear Regression

Pairplot

  • S-Section 03: Multiple Linear and Polynomial Regression [Notebook]

  • S-Section 03: Multiple Linear and Polynomial Regression

Pandas

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping [Notebook]

  • S-Section 01: Introduction to Web Scraping

  • Lab 02: More Pandas [Notebook]

  • Lab 02: Scraping [Notebook]

  • Lecture 3: Code Pandas + Beautiful Soup [Notebook]

  • Lecture 3: Pandas and Web Scraping

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

Pca

  • Lab 8: PCA

  • Lab 8: PCA [Notebook]

  • Advanced Sections 4: PCA

  • Lecture 14: PCA

  • Lecture 14: PCA [Notebook]

Pipeline

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification [Notebook]

  • S-Section 05: Logistic Regression, Multiple Logistic Regression, and KNN-classification

Plots

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

Polynomial Regression

  • S-Section 03: Multiple Linear and Polynomial Regression [Notebook]

  • S-Section 03: Multiple Linear and Polynomial Regression

  • Lab 4: Multiple and Polynomial Regression

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lab 04: Multiple and Polynomial Regression [Notebook]

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Predictors

  • Lecture 4: Introduction to Regression

Principal Components Analysis

  • S-Section 06: PCA and Logistic Regression [Notebook]

  • S-Section 06: PCA and Logistic Regression

Principle Component Analysis

  • Lab 8: PCA

  • Lab 8: PCA [Notebook]

Probabilities

  • Lecture 10: Logistic Regression [Notebook]

Python

  • Lab 01: YAML Environments, Python basics, Numpy [Notebook]

Qualitative Predictors

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

R-Square

  • Lecture 4: Introduction to Regression

R^2

  • Lecture 4: Introduction to Regression

Random Forest

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost [Notebook]

  • S-Section 08: Review Trees and Boosting including Ada Boosting Gradient Boosting and XGBoost

  • S-Section 06: Bagging and Random Forest [Notebook]

  • S-Section 07: Bagging and Random Forest

  • Lecture 16: Bagging, & Random Forest

Regression

  • Lecture 6: Multiple Linear Regression, Polynomial Regression

Regression Trees

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

Regularization

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lecture 11: Logistic Regression 2 [Notebook]

  • S-Section 04: Regularization and Model Selection [Notebook]

  • S-Section 04: Regularization and Model Selection

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Requests

  • Lab 2: Pandas and Scraping

Response Variable

  • Lecture 4: Introduction to Regression

RF

  • Lecture 16: Bagging, & Random Forest

Ridge

  • S-Section 04: Regularization and Model Selection [Notebook]

  • S-Section 04: Regularization and Model Selection

  • Advanced Section 2: Regularization

  • Advanced Sections 2: [Notebook]

Ridge Regression

  • Lecture 8: Regularization and EDA

Roc

  • Lecture 11: Logistic Regression 2 [Notebook]

Scikit-Learn

  • Lab 3: Scikit-learn for Regression [Notebook]

Scraping

  • Lab 2: Pandas and Scraping

  • Lecture 3: Pandas and Web Scraping

Seaborn

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

  • Lab 5: Exploratory Data Analysis, seaborn, more Plotting [Notebook]

Simple Linear Regression

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

Sklearn

  • Lecture 11: Logistic Regression 2 [Notebook]

  • Lecture 10: Logistic Regression [Notebook]

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

Statistical Model

  • Lecture 4: Introduction to Regression

Statsmodels

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

  • Lab 3: Matplotlib, Simple Linear Regression, kNN, array reshape

  • Lab 03: Extended Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

  • Lab 03: Prelab [Notebook]

  • Lab 03: Matplotlib, Simple Linear Regression, kNN, array reshape [Notebook]

Stochastic Gradient Descent

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver [Notebook]

  • S-Section 10: Feed Forward Neural Networks, Regularization, SGD Solver

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

T-Test.

  • Lecture 5: Linear Regression

Tensorflow

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 12: Building and Regularizing your first Neural Network [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

The Data Science Process

  • Lecture 2: Data Science Demo (repeat from Lecture 1) [Notebook]

  • Lecture 1: Data Science Demo [Notebook]

Train-Test

  • Lecture 4: Introduction to Regression

Training

  • S-Section 09: Feed forward neural networks [Notebook]

  • S-Section 09: Feed forward neural networks

Training And Testing Data Splitting

  • S-Section 02: kNN and Linear Regression [Notebook]

  • S-Section 02: kNN and Linear Regression

Trees

  • Lab 9: Decision Trees

  • Lab 9: Decision Trees [Notebook]

Variable Importance

  • Lecture 16: Bagging, & Random Forest

Variance Vs Bias

  • Lecture 15: Decision Trees

  • Lecture 15: Decision Trees [Notebook]

Visualization

  • Lecture 9: Visualization for Communication

Web Pages

  • Lab 13: Making websites! [Notebook]

Web Scraping

  • Lab 2: Pandas and Scraping

  • Lecture 3: Pandas and Web Scraping

Website Scraping

  • Lab 2: Pandas and Scraping

Websites

  • Lab 13: Making websites! [Notebook]

Weights

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras [Notebook]

  • Lab 11: Neural Network Basics - Introduction to tf.keras

Wix

  • Lab 13: Making websites! [Notebook]

Www

  • Lab 13: Making websites! [Notebook]

YAML

  • Lab 1: Python basics, YAML environments, Numpy

  • Lab 01: YAML Environments, Python basics, Numpy [Notebook]


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Machine Learning

关于机器学习,参看1.机器学习之KNN分类算法介绍: Stata和R同步实现(附数据和代码),2.机器学习对经济学研究的影响研究进展综述,3.回顾与展望经济学研究中的机器学习,4.最新: 运用机器学习和合成控制法研究武汉封城对空气污染和健康的影响! 5.Top, 机器学习是一种应用的计量经济学方法, 不懂将来面临淘汰危险!6.Top前沿: 农业和应用经济学中的机器学习, 其与计量经济学的比较, 不读不懂你就out了!7.前沿: 机器学习在金融和能源经济领域的应用分类总结,8.机器学习方法出现在AER, JPE, QJE等顶刊上了!9.机器学习第一书, 数据挖掘, 推理和预测,10.从线性回归到机器学习, 一张图帮你文献综述,11.11种与机器学习相关的多元变量分析方法汇总,12.机器学习和大数据计量经济学, 你必须阅读一下这篇,13.机器学习与Econometrics的书籍推荐, 值得拥有的经典,14.机器学习在微观计量的应用最新趋势: 大数据和因果推断,15.R语言函数最全总结, 机器学习从这里出发,16.机器学习在微观计量的应用最新趋势: 回归模型,17.机器学习对计量经济学的影响, AEA年会独家报道,18.回归、分类与聚类:三大方向剖解机器学习算法的优缺点(附Python和R实现),19.关于机器学习的领悟与反思,20.机器学习,可异于数理统计,21.前沿: 比特币, 多少罪恶假汝之手? 机器学习测算加密货币资助的非法活动金额! 22.利用机器学习进行实证资产定价, 金融投资的前沿科学技术! 23.全面比较和概述运用机器学习模型进行时间序列预测的方法优劣!24.用合成控制法, 机器学习和面板数据模型开展政策评估的论文!25.更精确的因果效应识别: 基于机器学习的视角,26.一本最新因果推断书籍, 包括了机器学习因果推断方法, 学习主流和前沿方法,27.如何用机器学习在中国股市赚钱呢? 顶刊文章告诉你方法!28.机器学习和经济学, 技术革命正在改变经济社会和学术研究,29.世界计量经济学院士新作“大数据和机器学习对计量建模与统计推断的挑战与机遇”,30.机器学习已经与政策评估方法, 例如事件研究法结合起来识别政策因果效应了!31.重磅! 汉森教授又修订了风靡世界的“计量经济学”教材, 为博士生们增加了DID, RDD, 机器学习等全新内容!32.几张有趣的图片, 各种类型的经济学, 机器学习, 科学论文像什么样子?33.机器学习已经用于微观数据调查和构建指标了, 比较前沿!34.两诺奖得主谈计量经济学发展进化, 机器学习的影响, 如何合作推动新想法!35.前沿, 双重机器学习方法DML用于因果推断, 实现它的code是什么?


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