Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it’s important that you have a basic understanding of how ...

Machine Learning is one of the approach of Artificial Intelligence in which Machines become capable of drawing intelligent decisions like humans by learning from its past experiences. In classical ...

Python libraries that can interpret and explain machine learning models provide valuable insights into their predictions and ensure transparency in AI applications. A Python library is a collection of ...

As one of the most popular, versatile, and beginner-friendly programming langauges, Python can be used for a variety of tasks from analyzing data to building websites. This workshop builds on the ...

With so many books on Python machine learning, making a choice is becoming increasingly difficult. You’re investing both your time and money to learn something that can open new career paths for you.

Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. If you’re starting a new machine learning or deep learning ...

Data science is an exciting and rapidly growing field that involves extracting insights and knowledge from data. To land a top data science job, it is important to have a solid foundation in key data ...

Python might be the default for most AI and machine learning development, but what about other popular languages? Here’s what you need to know about using Java, Rust, Go, or C#/.Net for AI/ML.