In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...

Python has a plethora of machine learning libraries, but the top 5 libraries are TensorFlow, Keras, PyTorch, Scikit-learn, and Pandas. These libraries offer a wide range of tools for various ...

The book leverages algorithms of machine learning in a unique way of describing real life applications. Though not mandatory, some experience with subject knowledge will fasten the learning process.

Discover five powerful Python libraries that enable data scientists to interpret and explain machine learning models effectively.

Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model.

As a Python library for machine learning, with deliberately limited scope, Scikit-learn is very good. It has a wide assortment of well-established algorithms, with integrated graphics.

Not necessarily for the data-science and machine-learning communities built around Python extensions like NumPy and SciPy, but as a general programming language.

Meta-Learning This is a less popular type of machine learning algorithm, but in many ways, it is both the easiest to understand and the most powerful.