Rich Sutton is old school king of RL. (see RL courses below). David Silver is the new school king of RL and superstar of Deepmind's AlphaGo (which uses Deep RL). Not much success in real world yet, but I'm still a fan as the questions and problems they're looking at feels a lot more applicable to real world than DL (e.g.
I've split this post into four sections: Machine Learning, NLP, Python, and Math. For future posts, I may create a similar list of books, online videos, and code repos as I'm compiling a growing collection of those resources too. What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
This model can subsequently be saved and deployed to Watson Machine Learning on IBM Bluemix and called for scoring in real-time. This tutorial is a continuation of the following logistic regression analysis, which creates, trains, saves and deploys a logistic regression model that predicts the possibility for a tent purchase based on age, sex, marital status, and job profession for an individual. In the templates folder, create a file called score.html and copy the following lines of code into the file you create. You have completed this tutorial that demonstrates how a deployed model in IBM Watson Machine Learning can be called in real time for scoring.
The new technologies like Machine Learning, Internet of Things, Deep Learning, NLP, Artificial Intelligence, Cloud, Big data and Predictive analytics are having a massive impact in India. This post is a Beginners Guide to Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing (NLP), Deep Learning, Big Data Analytics and Blockchain. Additionally, I have also listed some of the Best Online Courses for Machine Learning, Statistics, Data Science, IoT, and Big Data Analytics. The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.)
Quant Trading Using Machine Learning: Machine learning isn't just used in the tech industry. Finance professionals are using machine learning to build stronger financial models, and better inform investment decisions. This crash course in quantitative trading will help you apply machine learning techniques to sophisticated financial concepts. Learn By Example: Statistics and Data Science in R: Master one of the most popular programming languages used in data science and statistical computing. In just 9 hours, you'll understand the ins-and-outs of R, including how to apply it to the world of data. Learn By Example: Hadoop & MapReduce for Big Data Problems: In order to be a professional machine learning expert, you need to have professional working knowledge around big data. This course will teach you about Hadoop and MapReduce, two essential frameworks, with 14 hours of interactive learning. Byte Size Chunks: Java Object-Oriented Programming & Design: Java isn't just one of the oldest ...
Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This comprehensive course by Jose Portilla will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! You will learn how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!
This KDnuggets post will get your feet wet in the world of autonomous vehicle algorithms, providing an introductory insight into what to expect if you travel down this road, pun intended. Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. The purpose of this project is to use Python to play Grand Theft Auto 5. Check out the accompanying code here: Explorations of Using Python to play Grand Theft Auto 5.
This presents an exciting opportunity for database professionals and developers to either work with data scientists, or put on a data scientist hat to build predictive models that can help to assess credit loan risk, manage customer churn, reduce hospital admissions and more. In addition, if the data science project involves working with spatial data, temporal data or semi-structured data, you can leverage SQL Server capabilities that let you do this efficiently. By encapsulating the machine learning and AI models as part of the SQL Server stored procedure, it lets SQL Server serve AI with the data. With the R or Python code needed to work with the ML/AI models encapsulated in stored procedures, application developers can now leverage their ML/AI stored procedures as is (without requiring new libraries or learning new database access patterns).
Both look for patterns in data but, where data mining pulls data for a human to read, machine learning takes the data and looks for patterns, learning how to adjust the actions of the program accordingly. The news feed makes use of machine learning to make sure that your news feed is personal to you. I have split the book into two sections – the first gives you a bit of background in Machine Learning and what to expect, while the second part of the book is a practical example of a Machine Learning project that you can work through, right from installing Python to executing the project. I won't promise that machine learning is going to be easy and you do need to have a basic understanding of computer program, especially Python.
In this talk we will show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP etc. Spark has several Machine Learning algorithms built in and has excellent scalability. We will show how we solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. We will showcase our use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500 node Spark clusters.