Towards a Big data, Artificial Intelligence and Machine Learning driven ecosystem

source: Google

In today’s technological world of interconnected networks and big data analytics hardly any aspect of our lives is without the effects of data and data analysis.

We find machine learning and artificial intelligence applied in many industrial, services, business and scientific sectors of our economy. For example, healthcare services, medicine, transportation services, business analytics, computer vision, autonomous vehicles, aviation, defense, economic forecasting, algorithmic trading, computational finance, insurance , public services analytics, signal processing and computational sciences.

It is evident that the workforce of today must be armed with appropriate skills to navigate the AI driven process in the future.

In this article, we present a collection of useful resources for machine learning and big data analysis for applied data science. As well as targeted resources across various industrial sectors applying these technologies.

In recent years, progress in machine learning has been marked by the abundance of big data, faster computing architectures and the availability of powerful and user-friendly software packages, such as those in Python, R and Julia. A large majority of current Machine Learning models are already coded in these packages.

At Covee Network we have recognized the overarching need for expertise in providing turnkey solutions in the field by combining inter disciplinary skills from industrial domain expertise to machine learning and data science.

Applied Data Science AI/ML Resources

What is Data Science?

A Gentle Guide to Machine Learning

Notes on using Data Science & Artificial Intelligence

51 Artificial Intelligence (AI) Predictions For 2018

Becoming Human: Artificial Intelligence Magazine

Hitchhiker’s Guide to Data Science, Machine Learning, R, Python

Data Science Central, Deep Learning, AI, NLP

Data Sources

UCI ML Repository

List of Public Data Sources Fit for Machine Learning

U.S. Government’s open data

Awesome Public Datasets

Kaggle Datasets

StatLib — -Datasets Archive

AWS: datasets, public_datasets

GroupLens Research

IMF, World Bank

Databases

RDBMS SQL: MySQL, Oracle, Postgres, MS-SQL, Sqlite, Hive

NoSQL: MongoDB, BigTable, CouchDB, KDB+, Onetick, SciDB, Redis, RavenDb, Hbase, Neo4j, Cassandra

Blockchain: BigchainDB

Packages

Python packages:- scikit-Learn, Pandas, NLTK, XGBoost, numPy, Scipy, statsmodels, Keras, Lasagne, PyMC3, Theano, Tensorflow, NetworkX, gym, MXnet

R packages:- caret, glmnet, class::knn, FKF, XgBoost, MASS:Ida, e1071::svm, depmixS4, stats::loess, gam, stats::kmeans, stats::prcomp, MXnet, rstan, stats::prcomp, fastICA

C++ packages:- OpenCV, Caffe, CNTK, DSSTNE, LightGBM, CRF++, CRFSuite, m-net, OpenNN,

Java packages:- Weka, Mallet, H2O, MLib, Deeplearning4j, Mahout

Books

The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)

An Introduction to Statistical Learning (ISLR): with Applications in R (G. James et al)

Hands-On Machine Learning with Scikit-Learn & TensorFlow (Aurelien Geron)

Machine Learning in Action (Peter Harrington)

An Introduction to R ( W. N. Venables, D. M. Smith)

Think Python (Allen Downey)

Machine Learning Training

Andrew Ng, Machine Learning

Geoffrey Hinton, Deep Learning

Sebastian Thrun, Intro to Machine Learning

Tucker Balch, Machine Learning for Trading

Udacity: Self-Driving Car Engineer

KSEOW.com

colah.github.io

Hands-On Data Science Education

Machine Learning Competitions

Kaggle

NUMERAI

Topcoder

CrowdAnalytics

DrivenData

Domain Specific Resources

How does the data science landscape look like in 2018 across various industrial sectors?

Systematic/Algorithmic/Quantamental Trading:

Overview of Machine Learning in Trading

J.P. Morgan’s Guide: “Big Data and AI Strategies”

What are some algorithms behind high frequency trading?

Python Machine learning with SKLearn Tutorial for Investing — Intro

Machine Learning for FX market Prediction

2017’s Deep Learning Papers on Investing

Useful Data Sources: Quandl, Google, Yahoo, Quantopian, US Fundamentals Archive, Edgar

Cryptocurrency data: CCXT library Cryptocurrency API data, Poloniex, Quandl, kraken

Healthcare:

Machine Learning In Healthcare And Improved Wellness

Radiomics: Extracting more information from medical images using advanced feature analysis

How Radiologists are Using Machine Learning

Financial Services:

Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling

Data Mining Techniques in Fraud Detection

The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

Artificial Intelligence in Fraud Detection

AI and the new age of fraud detection

How AI And Machine Learning Are Used To Transform The Insurance Industry

Public Policy and Services:

Machine Learning Applications for Federal Government

AI-augmented Government — Using cognitive technologies to redesign public sector work

Machine learning: the power and promise of computers that learn by example

How AI Could Help the Public Sector

List of Public Data Sources Fit for Machine Learning

Automotive:

MIT 6.S094: Deep Learning for Self-Driving Cars

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

Driving style recognition method using braking characteristics based on hidden Markov model

NVIDIA: Training AI for Self-Driving Vehicles: the Challenge of Scale

Business Analytics:

6 Common Machine Learning Applications for Business

How to Apply Machine Learning to Business Problems

How to Apply Machine Learning to Business Problems

Big data and business intelligence trends 2017: Machine learning, data lakes and Hadoop vs Spark

Law

AI in Law and Legal Practice — A Comprehensive View of 35 Current Applications

A Primer on Using Artificial Intelligence in the Legal Profession

How artificial intelligence is revolutionising the way law firms and clients work together

Art

Art market ripe for disruption by algorithms

Machine Learning For Art Valuation. An interview with Ahmed Hosny.

Artificial Intelligence for Art valuation

Conclusion