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Blog on Machine Learning

Free Data Science Courses available from Coursera.

Below, it is a list of data science-related courses from Coursera, I hope you will find it useful: Introduction to Data Science in Python – University of Michigan The Data Scientist’s Toolbox –  Johns Hopkins University R Programming – Johns Hopkins University Getting and Cleaning Data – Johns Hopkins University Exploratory Data Analysis –  Johns Hopkins University Reproducible Research – Johns Hopkins University Statistical Inference – Johns Hopkins University Regression Models – Johns Hopkins University Practical Machine Learning – Johns Hopkins University Developing Data Products – Johns Hopkins University Introduction to Genomic Technologies – Johns Hopkins University Genomic Data Science with Galaxy – Johns Hopkins University Python for Genomic Data Science – Johns Hopkins University Command Line Tools for Genomic Data Science – Johns Hopkins University Algorithms for DNA Sequencing – Johns Hopkins University Bioconductor for Genomic Data Science – Johns Hopkins University Statistics for Genomic Data Science – Johns Hopkins University Machine Learning Foundations: A Case Study Approach – University of Washington Regression – University of Washington Classification – University of Washington Clustering & Retrieval – University of Washington Communicating Data Science Results – University of Washington Practical Predictive Analytics:…

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Numerai – basic intro with scikit-learn code.

In this post, I want to share, how simple it is to start competing in machine learning tournaments – Numerai. I will go step by step, line by line explaining what is doing what and why it is required. Numerai is a global artificial intelligence competition to predict the behavior. Numerai is a little bit similar to Kaggle but with clean datasets, so we can pass over long data cleansing process.  You just download the data, build a model, and upload your predictions, that’s it. To extract most of the data you would initially do some feature engineering, but for simplicity of this intro, we will pass this bit over.  One more thing we will pass on is splitting out validation set, the main aim of this exercise is to fit ‘machine learning’ model to training dataset. Later using fitted model, generate a prediction.  All together it shouldn’t take more…

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Intro to Machine Learning

What is definition of Machine Learning? Machine Learning subfield of science that provides computers with the ability to learn without being explicitly programmed.   The goal of Machine Learning is to develop learning algorithms that do the learning automatically without human intervention or assistance, just by being exposed to new data. The Machine Learning paradigm can be viewed as “programming by example”. This subarea of artificial intelligence intersects broadly with other fields like statistics, mathematics, physics, theoretical computer science and more. Machine Learning can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. Machine Learning can be a game changer in all these domains and is set to be a pillar of our future civilization. If one wants a program to predict something, one can run it through a Machine Learning algorithm with historical data and “train”…

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