Articles About Machine Learning

January 1, 2021 Machine Learning

Multinomial Logistic Regression With Python

Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary classification problems. Instead, the multinomial logistic regression algorithm is an extension to the logistic regression model that involves changing the loss function to cross-entropy loss […]

December 29, 2020 Machine Learning

Histogram-Based Gradient Boosting Ensembles in Python

Gradient boosting is an ensemble of decision trees algorithms. It may be one of the most popular techniques for structured (tabular) classification and regression predictive modeling problems given that it performs so well across a wide range of datasets in practice. A major problem of gradient boosting is that it is slow to train the model. This is particularly a problem when using the model on large datasets with tens of thousands of examples (rows). Training the trees that are […]

December 26, 2020 Machine Learning

Feature Selection with Stochastic Optimization Algorithms

Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset. This is called feature selection and there are many different types of algorithms that can be used. It is possible to frame the problem of feature selection as an optimization problem. In the case that there are few input features, all possible combinations of input features can be evaluated and the best subset found definitively. In the case of a […]

December 23, 2020 Machine Learning

Ensemble Learning Algorithm Complexity and Occam’s Razor

Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles. Taken at face value, the razor is a heuristic that suggests more complex hypotheses make more assumptions that, in turn, will make them too narrow and not generalize well. In machine learning, it suggests complex models like ensembles will overfit the training dataset and perform poorly on new data. In practice, ensembles are almost universally the type of model chosen […]

December 23, 2020 Machine Learning

How to Choose an Optimization Algorithm

Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks. There are perhaps hundreds of popular optimization algorithms, and perhaps tens of algorithms to choose from in popular scientific code libraries. This can make it challenging to know which algorithms to consider for a given optimization […]

December 20, 2020 Machine Learning

What Is Meta-Learning in Machine Learning?

Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning. Nevertheless, meta-learning might also refer to the manual process of model selecting and algorithm tuning performed by a practitioner on a machine learning project that modern automl algorithms seek to automate. It also refers to learning across […]

December 18, 2020 Beginner, Computer Vision, Deep Learning, Listicle, Machine Learning, NLP, Reinforcement Learning, Resource Leave a comment

A Review of 2020 and Trends in 2021 – A Technical Overview of Machine Learning and Deep Learning!

Introduction Data science is not a choice anymore. It is a necessity. 2020 is almost in the books now. What a crazy year from whichever standpoint you look at it. A pandemic raged around the world and yet it failed to dim the light on data science. The thirst to learn more continued unabated in our community and we saw some incredible developments and breakthroughs this year. From OpenAI’s mind-boggling GPT-3 framework to Facebook’s DETR model, this was a year […]

December 17, 2020 Machine Learning

Calculus Books for Machine Learning

Knowledge of calculus is not required to get results and solve problems in machine learning or deep learning. However, knowing some calculus will help you in a number of ways, such as in reading mathematical notation in books and papers, and in understanding the terms used to describe fitting models like “gradient,” and in understanding the learning dynamics of models fit via optimization such as neural networks. Calculus is a challenging topic as taught at a university level, but you […]

December 15, 2020 Computer Vision, Datasets, Deep Learning, Intermediate, Listicle, Machine Learning, NLP, Project Leave a comment

Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio!

Overview Here is a list of Top 15 Datasets for 2020 that we feel every data scientist should practice on The article contains 5 datasets each for machine learning, computer vision, and NLP By no means is this list exhaustive. Feel free to add other datasets in the comments below Introduction For the things we have to learn before we can do them, we learn by doing them -Aristotle I am sure everyone can attest to this saying. No […]

December 14, 2020 Machine Learning

Dynamic Classifier Selection Ensembles in Python

Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling. The technique involves fitting multiple machine learning models on the training dataset, then selecting the model that is expected to perform best when making a prediction, based on the specific details of the example to be predicted. This can be achieved using a k-nearest neighbor model to locate examples in the training dataset that are closest to the new example to be predicted, evaluating all models […]

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