Being able to perform machine learning in C will make you a very desirable hiring target. Not that you wouldn't be if you used any other language but, the truth is that machine learning in C is a great combination that is likely to give you access to very interesting positions! In this course, we focus on the practical part of machine learning--employing different C libraries. Several popular machine learning libraries currently exist--we'll review them and you'll become familiar with four of them. We use examples of standard machine learning algorithms implemented through the libraries.

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

Welcome to this course: Using Deep Learning To Make Decisions. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. Deep learning is synonymous with machine learning, and simply an advanced subset of that larger field. Technically speaking, deep learning is an umbrella term for a set of neural nets that consist of three or more layers; i.e. at least one hidden layer, and the visible layers of input and output. So what is deep learning capable and incapable of?

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation.

Math is absolutely necessary for the study of Machine Learning or Artificial Intelligence. Any deeper understanding of the concepts and algorithms in ML requires some basic maths knowledge. Linear Algebra, Statistics, Probability and Differential Calculus appear all throughout ML, and you've probably read that you should study these for 2 or 3 months before even getting into the basics of ML. Well, i'm here to tell you that this is not the best approach. Keep reading and I'll list some resources and show you ways how you can get this done a lot quicker than 2 or 3 months.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

We have been talking about the cybersecurity skills gap for a decade, but the pain is truly being felt now as businesses churn out new apps with insufficient security, says WhiteHat Security's Craig Hinkley. What is machine learning's role? See Also: Live Webinar Benchmarking Your Organization's Security Performance with Security Ratings Hinkley joined WhiteHat Security as CEO in early 2015, bringing more than 20 years of executive leadership in the technology sector to this role.

This post explains how to build your first Convolutional Neural Network (CNN) to detect between two image types: for example, a bunny or a puppy. Thanks to Google's new web tool, getting started building and prototyping your own neural network can be quite easy. Here is a link to the web-based application. It shows you the code and lets you run "paragraph by paragraph" (shift enter) jupyter notebook code to let you train a model and then test it. Find the Github public repo here.

Learn to use Python Artificial Intelligence for data science. Do you want to learn how to use Artificial Intelligence (AI) for automation? You will learn how to code in Python, calculate linear regression with TensorFlow, analyze credit card fraud and make a stock market prediction app. AI is code that mimics certain tasks. You can use AI to predict trends like the stock market.

Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning.