Addressing the setbacks of machine learning and providing value-added solutions

You’ve probably heard of machine learning a million times before. It might have been mentioned in a casual meeting, a random LinkedIn post sharing a miraculous artificial intelligence resource, a blog post, etc. You may have come across this phrase, but to what extent do you understand the meaning of machine learning?

If you’re in the field of information technology or data science, you’re quite obviously well-versed with this new technological addition. However, for those who have no background, the term has to be appropriately explained. Because of many unclear explanations about machine learning, the buzz created numerous myths that confused people. 

What Is Machine Learning?

Let’s put this out of the way. To dumb it down, machine learning involves learning from data. In simple terms, it helps process the data you’ve collected to provide better results. New businesses, big and small, have been popping out left and right. Likewise, each company collects information that piles up through time. Because of the vast collection, it isn’t easy to sift through them manually.

Machine learning can help you solve day-to-day problems by organizing your data and analyzing it for you. The term machine learning is part of artificial intelligence, but you can use both terms interchangeably—depending on how it’s used and the requirements. Imagine how much time you can save with the right algorithms.

History Of Machine Learning

The first few whispers of machine learning were introduced in 1949 by Donald Hebb when he wrote about the model of brain cell interaction in his book entitled The Organization of Behavior. However, it wasn’t fully explained by then. It was only in the 1950s when a breakthrough happened.

In the 1950s, a computer program of a game of checkers was created by Arthur Samuel from IBM. The program only required small storage, and he made a scoring system based on the position of the pieces on the board. This scoring function can calculate the chances of each side winning.

Over time, developments were made to improve machine learning. Today, people now enjoy speech and face recognition and camera filters. You can even make your machine learning infrastructure when you navigate to this site.

Common Pain Points And How To Strategize Against It

Just like any other program or project, there will always be issues that continue to recur. Here are a few common pain points from machine learning you can take note of:

1. Do You Need To Automate?

Because of so many articles released about machine learning, it’s getting quite difficult to differentiate whether or not the information is real. There are many programs and software that involve the use of machine learning. The choices are endless. But before choosing which software to utilize, first see what kind of problem you’re going to solve to find the right remedy.

There are common business problems that easy automation can solve, but some require a more in-depth study before going into automation that involves machine learning.

Remember this: machine learning can help your automation, but not all automation requires machine learning.

2. Quality Data

Machine learning only works when data is available. A lot of businesses depend on machine learning and artificial intelligence to make work easier for them. This includes finding the best solutions to problems in the workplace. Thus, when working with machine learning and programs related to it, the data provided should be clean, well-prepared, and complete to produce more accurate results.

3. Infrastructure Systems

Since machine learning works so fast, it requires a massive amount of data-churning capabilities. The amount of work it needs to get done also requires advanced hardware. Thus, before you go into machine learning and explore what it can offer, make sure you have updated tech and hardware so that there’s no limit to what you can do. 

Having the latest technology and purchasing it might be costly, but it’ll pay off once you successfully make use of it. If you can’t afford to buy the hottest drops in the market, try to upgrade a few hardware in your current system and expand your storage capacity. You’ll notice an immediate change in speed.

4. Implementation

Machine learning is quite complicated, and when a company chooses to delve into that area, there needs to be proper guidance from experts. Shifting to different types of programs can cause confusion and takes a lot of time for adjustment. Other things need to be covered, including security. Thus, a company should seek help from an implementation partner who can guide them through the process.

Implementation partners are IT experts who are well-versed with the matter at hand. They can help you decide what’s best for your company regarding machine learning and other programs. Likewise, they can detect anomalies, perform predictive analysis, and even model your needs more comfortably.

5. Number Of Skilled Resources

Machine learning and artificial intelligence are relatively new to the industry. This means only a handful of individuals are considered experts in this field. Thus, there’s a lack of human resources that can support all the companies that need help with machine learning. Because of the limited number of individuals who can provide the best support, the cost to outsource is expensive, especially if you want someone who can offer you the best work quality.

Will Machine Learning Destroy Humanity?

There are many funny stories surrounding machine learning, and one of them says it may destroy humanity. People are afraid that AI and machine learning might be too smart and can develop better knowledge than humans. Thus, they believe machine learning is a force to be reckoned with—something that will invalidate the very existence of human beings.

People find machine learning dangerous because of how it is portrayed in movies where robots are harming humans and taking over the world. This has to stop. While artificial intelligence has managed to slowly understand the brain system through artificial neural connections, there is no real possibility of machines dominating the world.

Conclusion

Machine learning is beneficial. While there are still portions of machine learning that need to be reviewed and studied, there’s no denying that it has made many people’s lives better. While the concept of machine learning is difficult to understand, in time, experts can relay information in a simpler way. It’s still in the development phase, and it might take years before experts discover the extent of what it can offer. Hopefully, this article helped a bit.

The post 5 COMMON PAIN POINTS WITH MACHINE LEARNING AND HOW TO SOLVE THEM appeared first on Artificial Intelligence.

A Python library is a collection of useful functions and code. Learn how to use different Python libraries for SEO projects and tasks here.

Python libraries are a fun and accessible way to get started with learning and using Python for SEO.

A Python library is a collection of useful functions and code that allow you to complete a number of tasks without needing to write the code from scratch.

There are over 100,000 libraries available to use in Python, which can be used for functions from data analysis to creating video games.

In this article, you’ll find several different libraries I have used for completing SEO projects and tasks. All of them are beginner-friendly and you’ll find plenty of documentation and resources to help you get started.

Why Are Python Libraries Useful for SEO?

Each Python library contains functions and variables of all types (arrays, dictionaries, objects, etc.) which can be used to perform different tasks.

For SEO, for example, they can be used to automate certain things, predict outcomes, and provide intelligent insights.

It is possible to work with just vanilla Python, but libraries can be used to make tasks much easier and quicker to write and complete.

Python Libraries for SEO Tasks

There are a number of useful Python libraries for SEO tasks including data analysis, web scraping, and visualizing insights.

This is not an exhaustive list, but these are the libraries I find myself using the most for SEO purposes.

Pandas

Pandas is a Python library used for working with table data. It allows for high-level data manipulation where the key data structure is a DataFrame.

DataFrames are similar to Excel spreadsheets, however, they are not limited to row and byte limits and are also much faster and more efficient.

The best way to get started with Pandas is to take a simple CSV of data (a crawl of your website, for example) and save this within Python as a DataFrame.

Once you have this stored in Python, you can perform a number of different analysis tasks including aggregating, pivoting, and cleaning data.

For example, if I have a complete crawl of my website and want to extract only those pages that are indexable, I will use a built-in Pandas function to include only those URLs in my DataFrame.

Requests

The next library is called Requests and is used to make HTTP requests in Python.

Requests uses different request methods such as GET and POST to make a request, with the results being stored in Python.

One example of this in action is a simple GET request of URL, this will print out the status code of a page:

You can then use this result to create a decision-making function, where a 200 status code means the page is available but a 404 means the page is not found.

You can also use different requests such as headers, which display useful information about the page like the content type or how long it took to cache the response.

There is also the ability to simulate a specific user agent, such as Googlebot, in order to extract the response this specific bot will see when crawling the page.

Beautiful Soup

Beautiful Soup is a library used to extract data from HTML and XML files.

Fun fact: The BeautifulSoup library was actually named after the poem from Alice’s Adventures in Wonderland by Lewis Carroll.

As a library, BeautifulSoup is used to make sense of web files and is most often used for web scraping, as it can transform an HTML document into different Python objects.

For example, you can take a URL and use Beautiful Soup together with the Requests library to extract the title of the page.

Additionally, using the find_all method, BeautifulSoup enables you to extract certain elements from a page, such as all a href links on the page:

Putting Them Together

These three libraries can also be used together, with Requests used to make the HTTP request to the page we would like to use BeautifulSoup to extract information from.

We can then transform that raw data into a Pandas DataFrame to perform further analysis.

Matplotlib and Seaborn

Matplotlib and Seaborn are two Python libraries used for creating visualizations.

Matplotlib allows you to create a number of different data visualizations such as bar charts, line graphs, histograms, and even heatmaps.

For example, if I wanted to take some Google Trends data to display the queries with the most popularity over a period of 30 days, I could create a bar chart in Matplotlib to visualize all of these.

Seaborn, which is built upon Matplotlib, provides even more visualization patterns such as scatterplots, box plots, and violin plots in addition to line and bar graphs.

It differs slightly from Matplotlib as it uses fewer syntax and has built-in default themes.

One way I’ve used Seaborn is to create line graphs in order to visualize log file hits to certain segments of a website over time.

This particular example takes data from a pivot table, which I was able to create in Python using the Pandas library, and is another way these libraries work together to create an easy-to-understand picture from the data.

Advertools

Advertools is a library created by Elias Dabbas that can be used to help manage, understand, and make decisions based on the data we have as SEO professionals and digital marketers.

Sitemap Analysis

This library allows you to perform a number of different tasks such as downloading, parsing, and analyzing XML Sitemaps to extract patterns or analyze how often content is added or changed.

Robots.txt Analysis

Another interesting thing you can do with this library is to use a function to extract a website’s robots.txt into a DataFrame, in order to easily understand and analyze the rules set.

You can also run a test within the library in order to check whether a particular user-agent is able to fetch certain URLs or folder paths.

URL Analysis

Advertools also enables you to parse and analyze URLs in order to extract information and better understand analytics, SERP, and crawl data for certain sets of URLs.

You can also split URLs using the library to determine things such as the HTTP scheme being used, the main path, additional parameters, and query strings.

Selenium

Selenium is a Python library that is generally used for automation purposes. The most common use case is testing web applications.

One popular example of Selenium automating a flow is a script that opens a browser and performs a number of different steps in a defined sequence such as filling in forms or clicking certain buttons.

Selenium employs the same principle as is used in the Requests library that we covered earlier.

However, it will not only send the request and wait for the response but also render the webpage that is being requested.

To get started with Selenium, you will need a WebDriver in order to make the interactions with the browser.

Each browser has its own WebDriver; Chrome has ChromeDriver and Firefox has GeckoDriver, for example.

These are easy to download and set-up with your Python code. Here is a useful article explaining the setup process, with an example project.

Scrapy

The final library I wanted to cover in this article is Scrapy.

While we can use the Requests module to crawl and extract internal data from a webpage, in order to pass that data and extract useful insights we also need to combine it with BeautifulSoup.

Scrapy essentially allows you to do both of these in one library.

Scrapy is also considerably faster and more powerful, completes requests to crawl, extracts and parses data in a set sequence, and allows you to shield the data.

Within Scrapy, you can define a number of instructions such as the name of the domain you would like to crawl, the start URL, and certain page folders the spider is allowed or not allowed to crawl.

Scrapy can be used to extract all of the links on a certain page and store them in an output file, for example.

You can take this one step further and follow the links found on a webpage to extract information from all the pages which are being linked to from the start URL, kind of like a small-scale replication of Google finding and following links on a page.

Final Thoughts

As Hamlet Batista always said, “the best way to learn is by doing.”

I hope that discovering some of the libraries available has inspired you to get started with learning Python, or to deepen your knowledge.

Python Contributions from the SEO Industry

Hamlet also loved sharing resources and projects from those in the Python SEO community. To honor his passion for encouraging others, I wanted to share some of the amazing things I have seen from the community.

As a wonderful tribute to Hamlet and the SEO Python community he helped to cultivate, Charly Wargnier has created SEO Pythonistas to collect contributions of the amazing Python projects those in the SEO community have created.

Hamlet’s priceless contributions to the SEO Community are featured.

Moshe Ma-yafit created a super cool script for log file analysis, and in this post explains how the script works. The visualizations it is able to display including Google Bot Hits By Device, Daily Hits by Response Code, Response Code % Total, and more.

Koray Tuğberk GÜBÜR is currently working on a Sitemap Health Checker. He also hosted a RankSense webinar with Elias Dabbas where he shared a script that records SERPs and Analyses Algorithms.

It essentially records SERPs with regular time differences, and you can crawl all the landing pages, blend data and create some correlations.

John McAlpin wrote an article detailing how you can use Python and Data Studio to spy on your competitors.

JC Chouinard wrote a complete guide to using the Reddit API. With this, you can perform things such as extracting data from Reddit and posting to a Subreddit.

Rob May is working on a new GSC analysis tool and building a few new domain/real sites in Wix to measure against its higher-end WordPress competitor while documenting it.

Masaki Okazawa also shared a script that analyzes Google Search Console Data with Python.

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