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 ...

In general, Machine Learning Algorithms is nicely structured and stands up to the name. There are chapters on regression, classification, support vector machines (SVM), decision trees, and clustering.

Like with movies, I don’t have one favorite machine learning (ML) algorithm, but a few favorites, each for its own reason. Here are some of my top few algorithms and models: Most elegant: The ...

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 ...

Machine Learning: A field of artificial intelligence, focused on the creation of algorithms, models and systems to perform tasks and generally to improve upon themselves in performing that task ...

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

Traditional machine learning algorithms, dataframe operations like groupby-aggregations, joins, and timeseries manipulation. Data ingestion like CSV and JSON parsing. And array computing like ...

Teaching yourself Python machine learning can be a daunting task if you don’t know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the ...

Teaching yourself Python machine learning can be a daunting task if you don’t know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the ...