Artificial Intelligence

How to Classify Star Wars Lego Images using CNN and Transfer Learning

Artificial Intelligence, Keras, Machine Learning, Python, TensorFlow / By

This tutorial is about training deep learning (DL) models to classify Star Wars Lego images. We use the TensorFlow library to create and compare the image classifiers. Are you looking for interesting deep learning projects that are suitable for beginners? Do not worry, this is not another MNIST image classification tutorial. Instead, we are going …

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[Cheat Sheet] 6 Pillar Machine Learning Algorithms

Leave a Comment / Algorithms, Artificial Intelligence, Computer Science, Machine Learning, Python / By

This machine learning cheat sheet gives you a visual overview of 6 must-know machine learning algorithms (and where to learn more). Linear Regression: train your linear model to predict output values. K-Means Clustering: apply it on unlabeled data to find clusters and patterns in your data. K-Nearest Neighbors: use a similarity metric to find the …

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Logistic Regression in Python Scikit-Learn

Algorithms, Artificial Intelligence, Computer Science, Data Science, Machine Learning, Python, Python One-Liners, Scikit-learn Library, sklearn / By

Logistic regression is a popular algorithm for classification problems (despite its name indicating that it is a “regression” algorithm). It belongs to one of the most important algorithms in the machine learning space. Linear Regression Background Let’s review linear regression. Given the training data, we compute a line that fits this training data so that …

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Random Forest Classifier with sklearn

Algorithms, Artificial Intelligence, Computer Science, Graph Theory, Machine Learning, Python, Scikit-learn Library, sklearn / By

Does your model’s prediction accuracy suck but you need to meet the deadline at all costs? Try the quick and dirty “meta-learning” approach called ensemble learning. In this article, you’ll learn about a specific ensemble learning technique called random forests that combines the predictions (or classifications) of multiple machine learning algorithms. In many cases, it …

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SVM sklearn: Python Support Vector Machines Made Simple

Algorithms, Artificial Intelligence, Computer Science, Machine Learning, Python, Python One-Liners, Scikit-learn Library, sklearn, The Numpy Library / By

Support Vector Machines (SVM) have gained huge popularity in recent years. The reason is their robust classification performance – even in high-dimensional spaces: SVMs even work if there are more dimensions (features) than data items. This is unusual for classification algorithms because of the curse of dimensionality – with increasing dimensionality, data becomes extremely sparse …

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Python Scikit-Learn Decision Tree [Video + Blog]

Leave a Comment / Algorithms, Artificial Intelligence, Computer Science, Graph Theory, Machine Learning, Python, sklearn, The Numpy Library / By

Decision Trees are powerful and intuitive tools in your machine learning toolbelt. Decision trees are human-readable – in contrast to most other machine learning techniques. You can easily train a decision tree and show it to your supervisors who do not need to know anything about machine learning in order to understand how your model …

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Neural Networks with SKLearn MLPRegressor

Algorithms, Artificial Intelligence, Computer Science, Machine Learning, Python, sklearn / By

Neural Networks have gained massive popularity in the last years. This is not only a result of the improved algorithms and learning techniques in the field but also of the accelerated hardware performance and the rise of General Processing GPU (GPGPU) technology. In this article, you’ll learn about the Multi-Layer Perceptron (MLP) which is one …

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K-Nearest Neighbors (KNN) with sklearn in Python

Algorithms, Artificial Intelligence, Computer Science, Machine Learning, Python, Python One-Liners, Scikit-learn Library / By

The popular K-Nearest Neighbors (KNN) algorithm is used for regression and classification in many applications such as recommender systems, image classification, and financial data forecasting. It is the basis of many advanced machine learning techniques (e.g., in information retrieval). There is no doubt that understanding KNN is an important building block of your proficient computer …

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[Tutorial] K-Means Clustering with SKLearn in One Line

Leave a Comment / Algorithms, Artificial Intelligence, Computer Science, Data Science, Machine Learning, Python, Scikit-learn Library, The Numpy Library / By

If there is one clustering algorithm you need to know – whether you are a computer scientist, data scientist, or machine learning expert – it’s the K-Means algorithm. In this tutorial drawn from my book Python One-Liners, you’ll learn the general idea and when and how to use it in a single line of Python …

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How to Get the Current Value of a Variable in TensorFlow?

Artificial Intelligence, Machine Learning, Python, TensorFlow / By

Problem Formulation Given a TensorFlow variable created with tf.Variable(). As this variable may have changed during the training process (e.g., using assign()), you want to get the current value of it. How to accomplish this in TensorFlow? Sessions Are Gone in TensorFlow 2 In TensorFlow 1, computations were performed within Sessions. That’s why many people …

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Sklearn fit() vs transform() vs fit_transform() – What’s the Difference?

Artificial Intelligence, Data Science, Machine Learning, Python, sklearn / By

Scikit-learn has a library of transformers to preprocess a data set. These transformers clean, generate, reduce or expand the feature representation of the data set. These transformers provide the fit(), transform() and fit_transform() methods. The fit() method identifies and learns the model parameters from a training data set. For example, standard deviation and mean for …

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How To Plot SKLearn Confusion Matrix With Labels?

Artificial Intelligence, Data Science, Data Visualization, Machine Learning, Math, Matplotlib, Python, sklearn / By

Summary: The best way to plot a Confusion Matrix with labels, is to use the ConfusionMatrixDisplay object from the sklearn.metrics module. Another simple and elegant way is to use the seaborn.heatmap() function. Note: All the solutions provided below have been verified using Python 3.9.0b5. Problem Formulation Imagine the following lists of Actual and Predicted values …

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