The Full Stack AI/ML Engineer toolkit needs to include web scraping, because it can improve predictions with new quality data. I thought, how can we angle "Web Scraping for Machine Learning", and I realized that Web Scraping should be essential to Data Scientists, Data Engineers and Machine Learning Engineers. The Full Stack AI/ML Engineer toolkit needs to include web scraping, because it can improve predictions with new quality data. Machine Learning inherently requires data, and we would be most comfortable, if we have as much high quality data as possible. But what about when the data you need is not available as a dataset?
This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.
There is a universal rule for buying stuff, "paying less money is better than paying more money". For personal reasons I needed to buy a used car but I was not in a hurry, I had time to think about it and find the best deal. When checking the local used car shops, they showed me cars in the range of 7.000 to 10.000 euro. This is a lot of money so I thought I should use my data science skills to find the best deal. First thing we need for a machine learning project is data.
"The Clock" is a 2010 art installation by Christian Marclay. It is an experimental film that features over 12,000 individual shots of clocks from movies and television, edited in such a way that the film itself functions as a clock. In this talk, we'll use modern machine learning models and video web scraping to recreate the concept behind "The Clock". We'll use Kubernetes to orchestrate building a modern video scraper, capable of getting around the walls of YouTube and Instagram to grab user content. We'll then use existing machine learning models to infer when clocks occur in videos, to create our own montage with the found internet video.
If you have a model that has acceptable results but isn't amazing, take a look at your data! Taking the time to clean and preprocess your data the right way can make your model a star. In order to look at scraping and preprocessing in more detail, let's look at some of the work that went into "You Are What You Tweet: Detecting Depression in Social Media via Twitter Usage." That way, we can really examine the process of scraping Tweets and then cleaning and preprocessing them. We'll also do a little exploratory visualization, which is an awesome way to get a better sense of what your data looks like!
-- Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc. This paper focuses on design and experiment of a new approach for botnet detection from streaming web server logs, motivated by its wide applicability, real-time protection capability, ease of use and better security of sensitive data. Our algorithm is inspired by a Principal Component Analysis (PCA) to capture correlation in data, and we are first to recognize and adapt Lanczos method to improve the time complexity of PCA-based botnet detection from cubic to sub-cubic, which enables us to more accurately and sensitively detect botnets with sliding time windows rather than fixed time windows. We contribute a generalized online correlation matrix update formula, and a new termination condition for Lanczos iteration for our purpose based on error bound and non-decreasing eigenvalues of symmetric matrices. On our dataset of an ecommerce website logs, experiments show the time cost of Lanczos method with different time windows are consistently only 20% to 25% of PCA. I. INTRODUCTION A bot is a software application that runs automated scripts over the Internet  to perform malicious tasks like DOS attack, website statistics skew, click fraud, price/information scraping, spam/rumor distribution, etc. Traditional single bots usually execute their tasks at a rate much higher than average human users to achieve their goal within a time limit. A botnet, as the name suggests, is a group of bots that work in a coordinated fashion. In contrast to single bots, a botnet, especially those large-scale botnets, they might request resources at a humanlike speed, but altogether they place a heavy burden on the servers and collect large amount of information. Because bots in a botnet behave humanlike, they are much harder to detect, and have become a key platform for many Internet attacks.
In a data science project, almost always the most time consuming and messy part is the data gathering and cleaning. Everyone likes to build a cool deep neural network (or XGboost) model or two and show off one's skills with cool 3D interactive plots. But the models need raw data to start with and they don't come easy and clean. But why gather data or build model anyway? The fundamental motivation is to answer a business or scientific or social question.
In a data science project, almost always the most time consuming and messy part is the data gathering and cleaning. Everyone likes to build a cool deep neural network (or XGboost) model or two and show off one's skills with cool 3D interactive plots. But the models need raw data to start with and they don't come easy and clean.
Gone are the days when people had to depend on the traditional media for news; now they are bombarded with news by huge number of online media outlets on the internet. So much that it's an information overload for the average person who has limited time to catch up on the news and stories. Social media now acts as a medium for news and it even makes the experience better for the users by customizing the feed to suit their reading habits. However, this massive proliferation of social media and web publishing comes with its own downsides. The widespread availability of easy-to-use content management systems such as WordPress has made it easier for anyone to be a web publisher.
Over the past few years, bots have started taking over parts of the tech world, with good bots like web crawlers indexing your site to boost your traffic and chat bots helping with more efficient communication in the office. Unfortunately, malicious bots are also on the rise, exposing vulnerabilities and stealing information. In fact, 2014 was the first year that bots were purported to have outnumbered actual people online. Distil Networks, a company that provides bot detection and mitigation services, recently raised 21 million in a Series C financing round to boost its efforts against bad bots. For those unfamiliar, a bot is simply a piece of software that runs automated scripts online.