Statistical Learning


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

@machinelearnbot

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Includes: 40.5 hours on-demand video 20 Articles 2 Supplemental Resources Full lifetime access Access on mobile and TV Certificate of Completion Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.


Machine Learning Algorithms: Which One to Choose for Your Problem

@machinelearnbot

When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you're like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don't ease the choice. In this article for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you'll find the structured overview of the main features of described algorithms.


Stop Doing Fragile Research

@machinelearnbot

Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more general and robust method, you have made the problem easier.


Time Series Analysis with Generalized Additive Models

@machinelearnbot

Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago. Hence, we would give more weight to recent than to older prices in predicting today's price.


Deep learning for activity recognition

VideoLectures.NET

Human activity recognition (HAR) plays an important role in people's daily life by learning and identifying high-level knowledge about human activity from raw sensor inputs. Conventional pattern recognition approaches have made tremendous progress on HAR tasks by adopting machine learning algorithms such as decision tree, random forest or support vector machine, but the fast development and advancement of deep learning have overpass the accuracy of traditional machine learning results. This seminar is focused on Deep learning applied to HAR using wearable sensors. Current architectures used and how to implement them for achieving good results will be explained. Limitations and new challenges will be also discussed.


Stop Doing Fragile Research

@machinelearnbot

Here's a story familiar to anyone who does research in data science or machine learning: (1) you have a brand-new idea for a method to analyze data (2) you want to test it, so you start by generating a random dataset or finding a dataset online.(3) You apply your method to the data, but the results are unimpressive. And you introduce a hyperparameter into your method so that you can fine-tune it, until (5) the method eventually starts producing gorgeous results. However, in taking these steps, you have developed a fragile method, one that is sensitive to the choice of dataset and customized hyperparameters. Rather than developing a more generaland robust method, you have made the problem easier.


The 10 Statistical Techniques Data Scientists Need to Master

@machinelearnbot

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go).


Email Spam Classifier Java Application with SPARK

#artificialintelligence

In this post we are going to develop an application for the purpose of detecting spam emails.The algorithm which will be used is Logistic Regression, implementation from SPARK MLib. No deep knowledge on the field is required as the topics are described from a high level perspective as possible. Full working code is provided together with a running application for further experiments on your choice of emails(please last section). Logistic Regression is an algorithm used for classification problems. In Classification problems we are given a lot of labeled data(example spam and not spam) and when a new example is coming we want to know which category it belongs to.


Regularization in Machine Learning – Towards Data Science

#artificialintelligence

One of the major aspects of training your machine learning model is avoiding overfitting. The model will have a low accuracy if it is overfitting. This happens because your model is trying too hard to capture the noise in your training dataset. By noise we mean the data points that don't really represent the true properties of your data, but random chance. Learning such data points, makes your model more flexible, at the risk of overfitting.


The 10 Statistical Techniques Data Scientists Need to Master

@machinelearnbot

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go).