Goto

Collaborating Authors

Statistical Learning


Machine Learning & Deep Learning in Python & R

#artificialintelligence

Free Coupon Discount - Machine Learning & Deep Learning in Python & R, Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R Hot & New Created by Start-Tech Academy English [Auto] Preview this Udemy Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes


Inference attacks: How much information can machine learning models leak?

#artificialintelligence

The widespread adoption of machine learning models in different applications has given rise to a new range of privacy and security concerns. Among them are'inference attacks', whereby attackers cause a target machine learning model to leak information about its training data. However, these attacks are not very well understood and we need to readjust our definitions and expectations of how they can affect our privacy. This is according to researchers from several academic institutions in Australia and India who made the warning in a new paper (PDF) accepted at the IEEE European Symposium on Security and Privacy, which will be held in September. The paper was jointly authored by researchers at the University of New South Wales; Birla Institute of Technology and Science, Pilani; Macquarie University; and the Cyber & Electronic Warfare Division, Defence Science and Technology Group, Australia.


Your Guide for the Commonly Used Machine Learning Algorithms

#artificialintelligence

We are currently living in such a period where computing has transformed immensely from large mainframes to personal computers to the cloud. The constant technological progress and the evolution in computing resulted in major automation. In this article, let's understand few commonly used machine learning algorithms. These can be used for almost any kind of data problem. This is used to estimate real values like the cost of houses, number of calls, total sales, and many more based on a continuous variable.


Hill Climbing and Simulated Annealing AI Algorithms

#artificialintelligence

Redeem Get Udemy Coupon What you'll learn Udemy Coupon Best Description Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that Hill Climbing and Simulated Annealing are the most well-regarded and widely used AI search techniques. A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.


[2021] Learn Python for Data Science A-Z

#artificialintelligence

Do you want to make your career in Data Science? Want to have a successful career and a life worth inspiring? All you need is the will to succeed and the passion to learn!!! Python being one of the most widely used languages is the new mantra for success. It is the number one tool for analytical professionals and is one of the top programming languages in the year 2019. Our aim is to make the students get acquainted with python and become proficient in the most popular programming language.


The 13 Best Machine Learning Certifications Online for 2021

#artificialintelligence

The editors at Solutions Review have compiled this list of the best machine learning certifications online to consider acquiring. Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications.


Stock Price Prediction Using Python & Machine Learning

#artificialintelligence

In this tutorial will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money! Actually this program is really simple and I doubt any major profit will be made from this program, but it's slightly better than guessing! In this video will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money!



Microsoft Will Soon Kill Flash on Windows 10 for Good

WIRED

The most recent big iOS update, which makes it easier to opt out of ads that track you across apps and web sites, has sent the digital marketing industry into a bit of a tizzy. That includes Facebook, which has been telling users that tracking helps keep its services "free of charge." Facebook is doing just fine, and choosing to preserve your privacy is not going to result in an Instagram service fee. Elsewhere in social media privacy news, Twitter rolled out a so-called Tip Jar this week that lets you send money to your favorite users. But it failed to vet how PayPal handles payments, potentially exposing users' home or email addresses when they send or receive a tip.


What Is a Gradient in Machine Learning?

#artificialintelligence

Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the field of calculus. This includes how to calculate a derivative and interpret the value. An understanding of the derivative is directly applicable to understanding how to calculate and interpret gradients as used in optimization and machine learning.