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What is machine learning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance. For more at Tinysoul

 

Why is machine learning important?

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are the different types of machine learning?

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

• Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
• Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
• Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
• Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

How does supervised machine learning work?

Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

• Binary classification: Dividing data into two categories.
• Multi-class classification: Choosing between more than two types of answers.
• Regression modeling: Predicting continuous values.
• Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

How does unsupervised machine learning work?

Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:

• Clustering: Splitting the dataset into groups based on similarity.
• Anomaly detection: Identifying unusual data points in a data set.
• Association mining: Identifying sets of items in a data set that frequently occur together.
• Dimensionality reduction: Reducing the number of variables in a data set.

Who's using machine learning and what's it used for?

Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's news feed. For more at machine learning

Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular group's posts, the recommendation engine will start to show more of that group's activity earlier in the feed.

Behind the scenes, the engine is attempting to reinforce known patterns in the member's online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly.

In addition to recommendation engines, other uses for machine learning include the following:

• Customer relationship management. CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
• Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.
• Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
• Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
• Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

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AI coins are cryptos that use artificial intelligence to improve the user experience, scalability, and security of blockchain networks. For more at ai crypto .Crypto AI coins support AI projects like decentralised marketplaces, market predictions, and portfolio management. AI-powered tokens can also give governance rights to their holders and for transactions. AI can be trained to spot illicit activities, market trends, investment opportunities, and other applications.

Hookah (also known as shisha, nargileh, or kalian) involves adults smoking flavored tobacco from a water pipe in which they pass a hose and take a puff among family or friends. It centers around conversations and promotes a more connectedness among those participating. Hookah is deeply rooted in a cultural tradition that has been present throughout generations among Indian, Persian, Turkish, Egyptian, and other Middle Eastern families. Its gesture is more than an entertaining social activity or means to relax, it’s a way that families, relatives, friends, and business associates in these cultures provide hospitality and strengthen bonds with one another.For more & best at  شيشة تقليدية.

 

THE HISTORY

 

16TH CENTURY

The first hookah in the form we know today dates back to 16th century India at a time when Indian glass manufacturing began as a result of the exporting of glass in India through the British East India Company. During this period, smoking tobacco also became popular amongst the noblemen in high society. In an attempt to purify smoke through water in a glass base called a “Shisha”, the Hookah was invented. These rough iterations of the now classic design featured wooden shafts with a secondary shaft affixed to the heart to smoke from. With this design in place, Hookah soon cemented itself as a way for noblemen to show their high social status.

 

17TH CENTURY

In the 17th century, Hookah (or Nargileh in this region) became a part of Persian culture where strong, dark leaf tobacco called Ajami was used. Craftsmen took pride in the aesthetics and redefined the look of the pipe through woodworking techniques setting hookah innovation in motion. For the first time, Nargileh also became accessible to everyone and an industry of hookah servants emerged. Even the Shah at the time had his own servants for nargileh.

 

18TH CENTURY

Shisha migrated into Turkish culture and during the 18th century, it continued to blossom through means of even more innovation. The now dated look of the shisha was phased out as blacksmiths began casting intricate brass designs and adorned the pipe with royal and religious markings. Their contribution in the form of this innovation was monumental as we still see ties to this in modern shisha construction today.
Shisha was prominent in Turkish high society and was a true symbol of status. It was smoked after royal dinners and at diplomatic meetings. Offering a shisha to a guest became an important sign of trust and withholding it could be taken as a serious insult.

 

19TH CENTURY

The Hookah tradition spread into the Middle East throughout the 19th century. In Egypt, traditional forms of tobacco seen prior were reformulated into Mu’Assel by mixing honey or molasses with the tobacco. Mu’Assel translates directly as “with honey” but this term generally refers to flavored tobacco as even dried lemon, grape, watermelon, and mint were added. During this time a purge port was integrated to the heart of the shisha to cool down the tobacco in an effort to have these flavors come forth more, thus changing the course of hookah history entirely. Shisha even integrated into society so deeply in these parts that shisha cafes were built to house the ever-growing popularity. Shisha promoted a community amongst the patrons, uniting all classes, races, and genders alike. It was used as a way for people to relax, socialize and strengthen bonds with one another. For more & best at  شيشة.

 

20TH CENTURY

Throughout the 20th Century, hookah traditions continued to deepen in cultures throughout India, Persia, Turkey, the Middle East, and in neighboring countries such as Israel, Armenia, and Pakistan with each culture putting their own spin on design elements of the pipes themselves. However, by the late 1900s, hookah has migrated to virtually every continent as immigrants of these countries brought this custom with them to share a piece of their culture to the new world.

 

21ST CENTURY

In the early 21st Century, a Hookah industry surfaced in the United States as early pioneers found ways to improve the quality of products using modern technology. Innovations in flavor choices beyond the traditional were also crafted to address the demands of current-day taste preference and variety.

 

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