Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Labs for the EE292D Edge ML class at Stanford

Solutions to assignments of the CS224W Machine Learning with Graphs course from Stanford University.

PyTorch implementation of the MRNet paper, developed for the MRNet Competition hosted by the Stanford ML Group

Machine Learning by Stanford University. Programming assignments.

Best free, open-source datasets for data science and machine learning projects. Top government data including census, economic, financial, agricultural, image datasets, labeled and unlabeled, autonomous car datasets, and much more. Data.gov NOAA - https://www.ncdc.noaa.gov/cdo-web/ atmospheric, ocean Bureau of Labor Statistics - https://www.bls.gov/data/ employment, inflation US Census Data - https://www.census.gov/data.html demographics, income, geo, time series Bureau of Economic Analysis - http://www.bea.gov/data/gdp/gross-dom... GDP, corporate profits, savings rates Federal Reserve - https://fred.stlouisfed.org/ curency, interest rates, payroll Quandl - https://www.quandl.com/ financial and economic Data.gov.uk UK Dataservice - https://www.ukdataservice.ac.uk Census data and much more WorldBank - https://datacatalog.worldbank.org census, demographics, geographic, health, income, GDP IMF - https://www.imf.org/en/Data economic, currency, finance, commodities, time series OpenData.go.ke Kenya govt data on agriculture, education, water, health, finance, … https://data.world/ Open Data for Africa - http://dataportal.opendataforafrica.org/ agriculture, energy, environment, industry, … Kaggle - https://www.kaggle.com/datasets A huge variety of different datasets Amazon Reviews - https://snap.stanford.edu/data/web-Am... 35M product reviews from 6.6M users GroupLens - https://grouplens.org/datasets/moviel... 20M movie ratings Yelp Reviews - https://www.yelp.com/dataset 6.7M reviews, pictures, businesses IMDB Reviews - http://ai.stanford.edu/~amaas/data/se... 25k Movie reviews Twitter Sentiment 140 - http://help.sentiment140.com/for-stud... 160k Tweets Airbnb - http://insideairbnb.com/get-the-data.... A TON of data by geo UCI ML Datasets - http://mlr.cs.umass.edu/ml/ iris, wine, abalone, heart disease, poker hands, …. Enron Email dataset - http://www.cs.cmu.edu/~enron/ 500k emails from 150 people From 2001 energy scandal. See the movie: The Smartest Guys in the Room. Spambase - https://archive.ics.uci.edu/ml/datase... Emails Jeopardy Questions - https://www.reddit.com/r/datasets/com... 200k Questions and answers in json Gutenberg Ebooks - http://www.gutenberg.org/wiki/Gutenbe... Large collection of books

My implementation for the MRNet competition hosted by the Stanford ML group

Lectures, exercises, and assignments for Stanford's ML class, in Scalala

Machine Learning Course by Stanford on Coursera (Andrew Ng)

Stanford ML Class programming exercises

Exercises submitted to the Machine Learning course by Stanford done through Coursera

Coursera/Stanford Machine Learning course assignments in python

Coursera Machine Learning (Andrew Ng) implements in Python

Notes on Coursera's ML Specialization by Stanford University and DeepLearning.AI and taught by Dr. Andrew Ng

Final Project for the CS22W: Machine Learning with Graphs course at Stanford University (http://web.stanford.edu/class/cs224w/) in Autumn '21. We applied graph ML techniques and networks (such as the Graph Convolutional Network, GraphSAGE Network and Graph Isomorphism Network) to the ogbl-ddi dataset (https://ogb.stanford.edu/docs/linkprop/#ogbl-ddi) for drug-drug interactions.

Proposed solution and baseline for CheXpert dataset, implemented in PyTorch. CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation by Stanford ML Group.

An attempt at the Bone X-Ray Deep Learning Competition (Stanford ML Group).

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All the execrcises from the stanford machine learning course

Course description: "Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas."

I had a good time studying supervised learning algorithms (especially during the Eid holidays of our country) and it was a delightful experience to learn from Andrew NG .

Machine Learning Course Offered By the University Of Stanford at Coursera.org (Andrew NG)

Solutions to Stanford ML Course problem sets (CS229)

Simple ML API built on FastApi for classifying breed of dog based on uploaded image with provided token authorization. PyTorch CNN model trained on Stanford Dogs dataset

Machine Learning class by Andrew Ng, provided by Coursera

Labs completed as part of the online ML course offered by Stanford

Stanford ML course by Andrew Ng

斯坦福机器学习课程（Machine Learning CS229）课后编程作业代码。

Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera