AI News, Data Science For Software Engineers

Data Science For Software Engineers

The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems.

For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, to understand natural language, or to detect fraudulent credit card transactions.

This course will introduce the fundamental set of techniques and algorithms that constitute machine learning as of today, ranging from classification methods like decision trees and support vector machines, over structured models like hidden Markov models, to clustering and matrix factorization methods for recommendation.

The course will not only discuss individual algorithms and methods, but also tie principles and approaches together from a theoretical perspective.

communication patterns and working set sizes for popular ML algos, and interactivity/flexibility requirements for data science

being able to parse really messy input data - the algorithm is often cake in comparison ref: human genome ;-)

the main types of learning algs, the intuition behind them, and the strengths and limitations of each in the context of REAL data.

Eight Sampling Techniques for Statistical & Data Science Modelling

In this video you will learn the different types of sampling techniques that you can use while building predictive models or data science models. You can use ...

Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems

David Vogel, Trustee (Voloridge Investment Management, LLC) Abstract: The introductory portion of this talk will review some state-of-the-art machine learning ...

Predictive Modelling Techniques | Data Science With R Tutorial

This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: ...

Brendan Herger | Machine Learning Techniques for Class Imbalances & Adversaries

PyData DC 2016 There are many areas of applied Machine Learning which require models optimized for rare occurrences (i.e. class imbalance), as well as ...

Hello World - Machine Learning Recipes #1

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's ...

YOLO Object Detection (TensorFlow tutorial)

You Only Look Once - this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object ...

Modeling Risk Assessment in Computational Grid using Machine Learning Techniques. By Dr. Sara Abd...

SRI_Webinar Abstract : Assessing risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the ...

Advanced Predictive Modelling in R | Predictive Modelling Techniques | What is Predictive Modelling

Watch Sample Recording ...

Learn Kaggle techniques from Kaggle #1, Owen Zhang

If you know kaggle.com(the biggest data scientists competition platform), you must have known Owen Zhang. He has competed in and won several high profile ...