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Cheat Sheet – 10 Machine Learning Algorithms & R Commands


From Bytes Cravings: “This article lists down 10 popular machine learning algorithms and related R commands (& package information) that could be used to create respective models. The objective is to represent a quick reference page for beginners/intermediate level R programmers who are working on machine learning related problems…. Following are the different ML algorithms included in this article: Linear Regression Logistic Regression K-Means Clustering K-Nearest Neighbors (KNN) Classification Naive … Continue Reading

Cheat Sheet: Algorithms for Supervised and Unsupervised Learning


A document with common machine learning algorithms for supervised and unsupervised learning, covering: K-nearest neighbour Naive Bayes Log-linear Perceptron Support vector machines K-means Mixture of Gaussians

iPython Notebooks

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From GitHub: “This repo contains various iPython notebooks I’ve created to experiment with libraries and work through exercises, and explore subjects that I find interesting.” The notebooks include: Popular Python data science libraries NumPy SciPy Matplotlib Pandas Statsmodels Scikit-learn Seaborn NetworkX PyMC NLTK DEAP Genism Machine Learning Exercises Tensorflow Deep Learning Exercises Spark Big Data Labs Miscellaneous

Learn Data Science

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From GitHub: “A collection of Data Science Learning materials in the form of iPython Notebooks. Associated data sets. The initial beta release consists of four major topics Linear Regression Logistic Regression Random Forests K-Means Clustering Each of the above has at least three iPython Notebooks covering Overview (an exposition of the technique for the math-wary) Data Exploration (the nuts and bolts of real world data wrangling) Analysis (using the technique to … Continue Reading

An Example Machine Learning Notebook


From GitHub: “In this notebook, I’m going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like. In addition to providing code examples, I also hope to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist. I will be following along with the … Continue Reading

Multiple Hypothesis Testing


From MultiThreaded: “In recent years, there has been a lot of attention on hypothesis testing and so-called ‘p-hacking’, or misusing statistical methods to obtain more ‘significant’ results…. This post introduces some of the interesting phenomena that can occur when we are dealing with testing hypotheses. First, we consider an example of a single hypothesis test which gives great insight into the difference between significance and “being correct”. Next, we look … Continue Reading

Perfect way to build a Predictive Model in less than 10 minutes


From Analytics Vidhya: ” have created modules on Python and R which can takes in tabular data and the name of target variable and BOOM! I have my first model in less than 10 minutes (Assuming your data has more than 100,000 observations). For smaller data sets, this can be even faster. The reason of submitting this super-fast solution is to create a benchmark for yourself on which you need to improve. … Continue Reading

Build a Predictive Model in 10 Minutes (using Python)


From Analytics Vidhya: “Last week, we published “Perfect way to build a Predictive Model in less than 10 minutes using R“. Any one can guess a quick follow up to this article. Given the rise of Python in last few years and its simplicity, it makes sense to have this tool kit ready for the Pythonists in the data science world. I will follow similar structure as previous article with my additional … Continue Reading

Essentials of Machine Learning Algorithms (with Python and R Codes)

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From Analytics Vidhya: “Today, as a data scientist, I can build data crunching machines with complex algorithms for a few dollors per hour. But, reaching here wasn’t easy! I had my dark days and nights…. The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. Through this guide, I will enable you to work on machine learning problems and … Continue Reading

Introduction to TensorFlow


This tutorial is a series of slides introducing TensorFlow for artificial intelligence and machine learning, and covering the following topics: Motivation and abstract model Gentle introduction: NN feedforward Not-as-gentle: learning with SGD Sequence-to-sequence learning