Data science is eating the world — Here’s how you can start exploring it
Towards a Big data, Artificial Intelligence and Machine Learning driven ecosystem
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
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
List of Public Data Sources Fit for Machine Learning
AWS: datasets, public_datasets
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
Hands-On Data Science Education
Machine Learning Competitions
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
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
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
The future of knowledge work is data-driven. And if you want to be part of it, you’d better start exploring machine learning and artificial intelligence. Hopefully, the suggested resources will help you get your hands dirty with data and software packages and introduce you to the magic of data science!
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