Tag: dimensionality reduction

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

Principal Component Analysis using R


From R-bloggers: “Curse of Dimensionality: One of the most commonly faced problems while dealing with data analytics problems such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios, fitting a model to the dataset, results in lower predictive … Continue Reading

Data Science and Machine Learning with Python – Hands On!


From Udemy: “Become a data scientists in the tech industry! Comprehensive data mining and machine learning course with Python & Spark. What Will I Learn? Develop using iPython notebooks Understand statistical measures such as standard deviation Visualize data distributions, probability mass functions, and probability density functions Visualize data with matplotlib Use covariance and correlation metrics Apply conditional probability for finding correlated features Use Bayes’ Theorem to identify false positives Make predictions … Continue Reading

Machine Learning A-Z: Hands-On Python & R In Data Science


From Udemy: “Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. What Will I Learn: Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP … Continue Reading

DataCamp – Unsupervised Learning in R


From DataCamp: “Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and … Continue Reading

Predictive Analytics World – Chicago


2017 Event: June 19-22, 2017 – Chicago, IL Co-Located with PAW Manufacturing Conference: http://www.predictiveanalyticsworld.com/mfg/2017/ From Predictive Analytics World: Predictive Analytics World is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners. The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail. 2017 Speakers

Introduction to Recommender Systems


Recommender systems have changed the way people find products, information, and even other people. We will study the most important recommender tools, including how they work, how to use them, evaluate them, and their strengths and weaknesses in practice.

scikit-learn algorithm cheat sheet


This flowchart is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.

Python Machine Learning


This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python.

Data Science at the Command Line


This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.