Modern Machine Learning Algorithms: Strengths and Weaknesses

In this guide, we’ll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don’t really explain the practical tradeoffs of each algorithm, which we hope to do here. We’ll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable…

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The Ultimate Python Seaborn Tutorial: Gotta Catch ‘Em All

In this step-by-step Seaborn tutorial, you’ll learn how to use one of Python’s most convenient libraries for data visualization. For those who’ve tinkered with Matplotlib before, you may have wondered, “why does it take me 10 lines of code just to make a decent-looking histogram?” Well, if you’re looking for a simpler way to plot attractive charts, then…

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The 5 Levels of Machine Learning Iteration

Can you guess the answer to this riddle? If you’ve studied machine learning, you’ve seen this everywhere… If you’re a programmer, you’ve done this a thousand times… If you’ve practiced any skill, this is already second-nature for you… Nope, it’s not overdosing on coffee… It’s… iteration! Yes, iteration as in repeating a set of tasks to achieve a result. Wait, isn’t that just… the dictionary…

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R vs Python for Data Science: Summary of Modern Advances

Recently, some of our readers have been asking us about the best programming language for data science. Immediately, R and Python both come to mind… but which of these two giants to choose? We felt that this was a good time to address this question because we recently watched an excellent presentation on recent advances of…

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Python Machine Learning Tutorial, Scikit-Learn: Wine Snob Edition

In this end-to-end Python machine learning tutorial, you’ll learn how to use Scikit-Learn to build and tune a supervised learning model! We’ll be training and tuning a random forest for wine quality (as judged by wine snobs experts) based on traits like acidity, residual sugar, and alcohol concentration. Before we start, we should state that this guide is meant for beginners…

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Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python

In this step-by-step Keras tutorial, you’ll learn how to build a convolutional neural network in Python! In fact, we’ll be training a classifier for handwritten digits that boasts over 99% accuracy on the famous MNIST dataset. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. Our goal is…

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21 Must-Know Machine Learning Interview Questions and Answers

The following machine learning interview questions and answers are broken in 9 major topics, as outlined in our guide to machine learning. Table of Contents The Big Picture Optimization Data Preprocessing Sampling & Splitting Supervised Learning Unsupervised Learning Model Evaluation Ensemble Learning Business Applications For an additional 100 ML interview questions and answers, check out…

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5 Genius Python Deep Learning Libraries

Deep learning is an exciting subfield at the cutting edge of machine learning and artificial intelligence. Deep learning has led to major breakthroughs in exciting subjects just such computer vision, audio processing, and even self-driving cars. In this guide, we’ll be reviewing the essential stack of Python deep learning libraries. These packages support a variety of deep learning architectures such as feed-forward…

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How to Learn Math for Data Science, The Self-Starter Way

Do you need to have a math Ph.D to become a data scientist? Absolutely not! This guide will show you how to learn math for data science and machine learning without taking slow, expensive courses. How much math you’ll do on a daily basis as a data scientist varies a lot depending on your role. Keep reading to find…

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5 Heroic Python NLP Libraries

Natural language processing (NLP) is an exciting field in data science and artificial intelligence that deals with teaching computers how to extract meaning from text. In this guide, we’ll be touring the essential stack of Python NLP libraries. These packages handle a wide range of tasks such as part-of-speech (POS) tagging, sentiment analysis, document classification, topic…

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