There are many paths into the field of machine learning and most start with theory. If you are a programmer then you already have the skills to decompose problems into their constituent parts and to prototype small projects in order to learn new technologies, libraries and methods. These are important skills for any professional programmer […]
The source code of a program should be readable to humans. Making it run correctly is only half of its purpose. Without a properly commenting code, it would be difficult for one, including the future you, to understand the rationale and intent behind the code. It would also make the code impossible to maintain. In […]
We have previously seen that calculus is one of the core mathematical concepts in machine learning that permits us to understand the internal workings of different machine learning algorithms. Calculus, in turn, builds on several fundamental concepts that derive from algebra and geometry. The importance of having these fundamentals at hand will become even more […]
XGBoost is a powerful and popular implementation of the gradient boosting ensemble algorithm. An important aspect in configuring XGBoost models is the choice of loss function that is minimized during the training of the model. The loss function must be matched to the predictive modeling problem type, in the same way we must choose appropriate […]
Deep learning neural network models are fit on training data using the stochastic gradient descent optimization algorithm. Updates to the weights of the model are made, using the backpropagation of error algorithm. The combination of the optimization and weight update algorithm was carefully chosen and is the most efficient approach known to fit neural networks. […]
The Gaussian Processes Classifier is a classification machine learning algorithm. Gaussian Processes are a generalization of the Gaussian probability distribution and can be used as the basis for sophisticated non-parametric machine learning algorithms for classification and regression. They are a type of kernel model, like SVMs, and unlike SVMs, they are capable of predicting highly […]
Voting is an ensemble machine learning algorithm. For regression, a voting ensemble involves making a prediction that is the average of multiple other regression models. In classification, a hard voting ensemble involves summing the votes for crisp class labels from other models and predicting the class with the most votes. A soft voting ensemble involves […]
Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate […]
You must prepare your raw data using data transforms prior to fitting a machine learning model. This is required to ensure that you best expose the structure of your predictive modeling problem to the learning algorithms. Applying data transforms like scaling or encoding categorical variables is straightforward when all input variables are the same type. […]
It is important that beginner machine learning practitioners practice on small real-world datasets. So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be used by beginner practitioners to quickly test, explore, and practice data preparation and modeling techniques. A practitioner can confirm […]
