Web Scraping Essential Skills in less than one hour!!!
Practical Web Scraping Tips and Tricks
Beginner Level Python
If you want to be a creative data scientist, web scraping is an indispensible capability you should learn. In this effort, you should start from understanding and establishing essential skills and tools of web scraping.
Practice a lot with them through solving real-life problems.
This is the way to proceed in a more natural evolution and much more intuitively so that you are able to ask the right questions and come up with solutions.
“Web Scraping with Python 101: Build Scrapy Essential Skills” is the course aimed at these fundamentals through Scrapy, Python’s popular web scraping framework.
This is the starter course of a series to master web-scraping and Scrapy from basic skills to advanced concepts, from deep insights on the tools to the most practical real-life data science example utilizing web scraping on this platform, a depth and perspective unique to this course series that collectively have gathered more than 10000 students in months.
Two remaining questions:
First, who is this course aiming?
This course is for beginners. Not for beginners to programming, but beginners to Web Scraping. Persons who have seen it, thought about learning it, have limited time. Its about showing them how to start, and proceed and that web scraping is not earth science.
Second, what exactly is in this course?
In this course;
We will start with what is web scraping and why it is important.
We fill define the 3 pillars of webscraping: crawling, scraping and keeping the connection.
We will run our pillars on a living ‘amazon’ example.
We will finish with practical recommendations on each pillar and on a guide to how to proceed.
A fully refined course to perfectly fit your busy schedule.
Last but not the least; be sure to watch the course video on this very landing page.
See you in the lectures!
Who this course is for:
Beginner Python Developers curious about web scraping
Anybody who wants to learn about web scraping concepts
Ever since digitalization took center stage, various new-age technologies have come to the fore and benefited sectors in a dramatic manner. Though the emergence has not only enticed humankind to embrace software and applications to manage their day-to-day chores but pushed various organizations to adopt performance-driven solutions. According to a cybersecurity company, there are 8.93 million mobile applications today, with the Google Play Store having 3.553 million apps, the Apple App Store having 1.642 million apps, and Amazon having 483 thousand apps. Traditionally, the focus of IT organizations has been entirely on technology development; however, exposure to apps and software has enabled individuals and businesses to achieve a given goal and execute the function. In this context, performance testing and monitoring came to the rescue, allowing IT solution providers and enterprises working on business-specific solutions to help and resolve issues that could lead to a poor user experience and revenue loss.
The early phase of performance testing and monitoring methods was limited to manual procedures, but the advent of innovative technologies such as artificial intelligence (AI) and machine learning (ML) enhanced and transformed the testing and monitoring process for the better. Especially the introduction of ML (a subset of AI) has enabled computer systems to learn, identify patterns, and make predictions without being programmed. Machine learning algorithms can be trained on large datasets of performance data to automatically identify anomalies, predict performance issues, and suggest optimization strategies. According to Market Research, the global machine learning market is poised to reach INR 7632.45 billion by 2027 at a CAGR of 37.12% during the forecast period 2021-2027.
The utilization of machine learning in testing makes the process more competent and dependable. And provide several benefits, such as improved accuracy, limited test maintenance, aid in test case writing and API testing, test data generation, and reduced UI-based testing. As technology evolves, the way we develop and test also needs to change, and testing in production itself is possible when ML can show future disruptions in advance to mitigate. Testing in production means code coverage of exactly what is needed without additional spending on the test environment. Thus, ML has become a vital player in improving performance testing and monitoring, eradicating the need for creating long-winded test procedures and reducing the time spent maintaining tests.
Ways to Improve Performance Testing and Monitoring
During testing, an application may display a variety of performance issues, such as an increased latency, systems that hang, freeze, or crash, and a decrease in throughout. As a result, machine learning emerged as a solution and can be used to track the source of a problem in software. Furthermore, ML’s capabilities are useful for current concerns and anticipating future values, and comparing them to those acquired in real-time.
In addition, the critical advantage of ML algorithms is that they learn and improve over time. The model can automatically alter in reaction to data, assisting in defining what “normal” is from week to week or month to month. Not only on time series data but ML correlation algorithms can also be used to find code-level issues causing resource abuse. This means that we can consider new data patterns and generate predictions and projections that are more exact than those based on the original data pattern. So let’s delve into some of the ways in which machine learning can improve performance testing and monitoring.
Predictive Analytics: Machine learning algorithms can be trained to forecast future performance concerns based on the collected data. This can assist the organization in proactively identifying and mitigating potential performance issues before they affect users.
Automated Anomaly detection: Machine learning algorithms can learn regular application performance patterns by analyzing performance measures like response time, throughput, and resource utilization. Once trained, the algorithm can detect anomalies such as unexpected spikes or decreases in performance and alert developers and operators to the problem.
Root Cause Analysis and Optimization: Performance data can be analyzed by machine learning techniques to pinpoint the underlying causes of performance problems. This can save time and effort for developers and operators who would otherwise need to detect and fix the problem manually. Thus, it can help teams optimize resource usage and improve performance.
Correlation and Causation: ML correlation and causation techniques can identify and quantify the relationship between resources and help build a causal graph to show how they affect performance.
Real-time Monitoring: Real-time performance data analysis by machine learning algorithms can predict performance problems in advance and alert. Firms can respond to concerns more rapidly and with less impact on users.
In addition, to implement machine learning for performance testing and monitoring, businesses must gather and store vast volumes of performance data, filter data for accuracy, train machine learning models, and deploy them as needed. It is critical to highlight that machine learning is not a panacea and should be augmented with traditional performance testing and monitoring approaches to achieve the best outcomes.
Technology: Pathway to Boost Performance
In the modern era, with the growing number of software and applications, businesses are discovering that software performance at par is not just a perk for customers but a necessity. The inability to achieve the desired outcome can result in financial loss and poor customer experience that should not be overlooked. This is where the need for machine learning has become essential, which can significantly improve performance testing and monitoring by automating anomaly detection, providing predictive analytics, enabling root cause analysis, optimizing resource usage, and enabling real-time monitoring. Furthermore, as software systems become more sophisticated, machine learning will become an increasingly important tool for ensuring optimal performance and user experience.
Every week, the top AI labs globally — Google, Facebook, Microsoft, Apple, etc. — release tons of new research work, tools, datasets, models, libraries and frameworks in artificial intelligence (AI) and machine learning (ML).
Interestingly, they all seem to have picked a particular school of thought in deep learning. With time, this pattern is becoming more and more clear. For instance, Facebook AI Research (FAIR) has been championing self-supervised learning (SSL) for quite some time, alongside releasing relevant papers and tech related to computer vision, image, text, video, and audio understanding.
Even though many companies and research institutions seem to have their hands on every possible area within deep learning, a clear pattern is emerging. But, of course, all of them have their favourites. In this article, we will explore some of the recent work in their respective niche/popularised areas.
A subsidiary of Alphabet, DeepMind remains synonymous with reinforcement learning. From AlphaGo to MuZero and the recent AlphaFold, the company has been championing breakthroughs in reinforcement learning.
AlphaGo is a computer program to defeat a professional human Go player. It combines an advanced search tree with deep neural networks. These neural networks take a description of the Go board as input and process it through a number of different network layers containing millions of neuron-like connections. The way it works is — one neural network ‘policy network’ selects the next move to play, while the other neural network, called the ‘value network,’ predicts the winner of the game.
Taking the ideas one step further, MuZero matches the performance of AlphaZero on Go, chess and shogi, alongside mastering a range of visually complex Atari games, all without being told the rules of any game. Meanwhile, DeepMind’s AlphaFold, the latest proprietary algorithm, can predict the structure of proteins in a time-efficient way.
GPT-3 is one of the most talked-about transformer models globally. However, its creator OpenAI is not done yet. In a recent Q&A session, Sam Altman spoke about the soon to be launched language model GPT-4, which is expected to have 100 trillion parameters — 500x the size of GPT-3.
Besides GPT-4, Altman gave a sneak-peek into GPT-5 and said that it might pass the Turing test. Overall, OpenAI looks to achieve artificial general intelligence with its series of transformer models into new areas.
Today, GPT-3 competes with the likes of EleutherAI GPT-j, BAAI’s Wu Dao 2.0 and Google’s Switch Transformer, among others. Recently, OpenAI launched OpenAI Codex, an AI system that translates natural language into code. It is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.
Facebook is ubiquitous to self-supervised learning techniques across domains via fundamental, open scientific research. It looks to improve image, text, audio and video understanding systems in its products. Like its pretrained language model XLM, self-supervised learning is accelerating important applications at Facebook today — like proactive detection of hate speech. Further, its XLM-R, a model that leverages RoBERTa architecture, improves hate speech classifiers in multiple languages across Instagram and Facebook.
Facebook believes that self-supervised learning is the right path to human-level intelligence. It accelerates research in this area by sharing its latest work publicly and publishing at top conferences, alongside organising workshops and releasing libraries. Some of its recent work in self-supervised learning include VICReg, Textless NLP, DINO, etc.
Google is one of the pioneers in automated machine learning (AutoML). It is advancing AutoML in highly diverse areas like time-series analysis and computer vision. Earlier this year, Google Brain researchers introduced a new way of programming AutoML based on symbolic programming called PyGlove. It is a general symbolic programming library for Python, used for implementing symbolic formulation of AutoML.
Some of its latest products in this area include Vertex AI, AutoML Video Intelligence, AutoML Natural Language, AutoML Translation, and AutoML Tables.
On-device machine learning comes with privacy challenges. To tackle the issue, Apple, in the last few years, has ventured into federated learning. For those unaware, federated learning is a decentralised form of machine learning. It was first introduced by Google researchers in 2016 in a paper titled, ‘Communication Efficient Learning of Deep Networks for Decentralized Data,’ but has been widely adopted by various players in the industry to ensure smooth training of machine learning models on edge, alongside maintaining the privacy and security of user data.
In 2019, Apple, in collaboration with Stanford University, released a research paper called ‘Protection Against Reconstruction and Its Applications in Private Federated Learning,” which showcased practicable approaches to large-scale locally private model training that were previously impossible. The research also touched upon theoretical and empirical ways to fit large-scale image classification and language models with little degradation in utility.
Check out other top research papers in federated learning here.
Apple designs all its products to protect user privacy and give them control of their data. Despite the setbacks, the tech giant is working on various innovative ways to offer privacy-focused products and apps by leveraging federated learning and decentralised alternative techniques.
Microsoft Research has become one of the number one AI labs globally, pioneering machine teaching research and technology in computer vision and speech analysis. It offers resources across the spectrum, including intelligence, systems, theory and other sciences.
Under intelligence, it covers research areas like artificial intelligence, computer vision, search and information retrieval, among others. In the systems, the team offers resources in quantum computing, data platforms and analytics, security, privacy and cryptography, and more. Currently, it has become a go-to platform for attending lecture series, sessions and workshops.
Earlier, Microsoft launched free machine learning for beginners to teach students the basics of machine learning. For this, Azure Cloud advocates and Microsoft student ambassador authors, contributors, and reviewers put together the lesson plan that uses pre-and-post lesson quizzes, infographics, sketch notes, and assignments to help students adhere to machine learning skills.
Amazon has become one of the leading research hubs for transfer learning methods due to its exceptional work in the Alexa digital assistant. Since then, it has been pushing research in the transfer learning space incredibly, be it to transfer knowledge across different language models, techniques, or better machine translation.
There have been several research works implemented by Amazon, especially in transfer learning. For example, in January this year, Amazon researchers proposed ProtoDA, an efficient transfer learning for few-shot intent classification.
Check out more resources related to transfer learning from Amazon here.
While IBM pioneered technology in many machine learning areas, it lost a leadership position to other tech companies. For example, in the 1950s, Arthur Samuel of IBM developed a computer programme for playing checkers. Cut to the 2020s, IBM is pushing its research boundaries in quantum machine learning.
The company is now pioneering specialised hardware and building libraries of circuits to empower researchers, developers and businesses to tap into quantum as a service through the cloud, using preferred coding language and without the knowledge of quantum computing.
By 2023, IBM looks to offer entire families of pre-built runtimes across domains, callable from a cloud-based API, using various common development frameworks. It believes that it has already laid the foundations with quantum kernel and algorithm developers, which will help enterprise developers explore quantum computing models independently without having to think about quantum physics.
In other words, developers will have the freedom to enrich systems built in any cloud-native hybrid runtime, language, and programming framework or integrate quantum components simply into any business workflow.
The article paints a bigger picture of where the research efforts of the big AI labs are heading. Overall, the research work in deep learning seems to be going in the right direction. Hopefully, the AI industry gets to reap the benefits sooner than later.