The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing—famous for breaking the Nazi's ENIGMA code during WWII—proposes to answer the question 'can machines think?' and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.
1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.
1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.
1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.
1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).
2011: IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy!
2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.
2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported $400 million.
Artificial Intelligence Course :
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The amount of data available to businesses is nearly limitless. For those who enjoy organising, viewing, and using data to solve issues, offer business insight, and make decisions, a career in business analytics may be a suitable fit.

For instance, data-driven insights can assist businesses in evolving product development in ways that proactively answer the needs of their customers. Additionally, they can assist a business in determining the types of modifications that have historically been successful in gaining or losing clients. Attracting new clients gets more difficult as business competition rises. Data can be used by analysts to find strategies for retaining current customers while attracting new ones.
Business analytics also enables businesses to categorise their customers according to variables like their geography, age, and product usage. This not only assists with message and advertising opportunities, but it also enables a company to identify the most lucrative consumer groupings. Businesses that are able to recognise and concentrate on valuable clients get increased return on investment (ROI).
Business Analytics Course :
https://www.youtube.com/watch?v=_1GKn4xrioc

Ethical hacking is the act of attacking a network for the purpose of finding its weaknesses. It is done by agreement between the network or computer owners and the hacker. This type of hacker is known as a white hat hacker. This practice perfectly legal and is a legitimate business. The agreement between the hacker and the network owner is usually very detailed and outlines exactly how the attack will take place and the results are normally kept secret. Now a black hat hacker is the exact opposite. This hacker is attacking networks for the sole purpose of doing damage and making money. No warning and no mercy. They take over computers, steal or take data hostage and even reveal information to the public.
Ethical Hacking Training :
https://www.youtube.com/watch?v=nX9x9GICmL0

Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.
Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn’t specialized for any specific problems. This versatility, along with its beginner-friendliness, has made it one of the most-used programming languages today. A survey conducted by industry analyst firm RedMonk found that it was the second-most popular programming language among developers in 2021.

It employs elegant syntax, making the programmes easy to comprehend.
It's an easy-to-use language that makes getting the software to function a breeze.
The community support and the big standard library.
Python's interactive mode makes testing codes a breeze.
It is also straightforward to extend Python code by adding additional modules written in other compiled languages such as C++ or C.
Python is a programmable language that may be embedded into applications to provide a programmable interface.
Allows developers to run their programmes on Windows, Mac OS X, UNIX, and Linux systems.
In a few categories, it is free software. It costs nothing to use or download Pythons, or to add them to your system.
Python Data Science Course :
https://www.youtube.com/watch?v=vVnkU1fTM_w

The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions.

Machine learning, like the human brain, relies on input, such as training data or knowledge graphs, to grasp entities, domains, and their connections. Deep learning can begin once entities have been defined.

Observations or data, such as examples, direct experience, or instruction, are used to start the machine learning process. It searches for patterns in data so that it can draw conclusions based on the examples presented. The basic goal of machine learning is to allow computers to learn without human intervention and change their activities accordingly.
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Machine Learning Course :
https://www.youtube.com/watch?v=3GAunHmofok

The essential Power BI data types are number day types and the date and time data types. Whole number data type. Decimal data type. Currency data type.
In the computer programming context, the Power BI data types are an attribute of data. This attribute informs the analyst about the programmer’s intention of using the data. These data types provide a set of values that gives value to an expression.

The Power BI data types can be classified into various categories. These are as follows:-

Text.
Number
Date and time
Boolean or logical.
The essential Power BI data types are number day types and the date and time data types.

They are discussed in the following manner-

Number Data Types – These data types can be used for aggregating and assimilating relevant organization information. Several functions like adding, subtracting, calculating an average and finding fractional values can be done with this data type. The numeric data type can be further categorised as follows-
Whole number data type.
Decimal data type.
Currency data type.
Percentage data type.
Date and time data types – This data type provides a huge source of information. The right data, time, month, year and other information can be easily extracted from these data types. The date and time data type formats can be categorised further into the following-
Date/ Time
Date
Time
Date/ Time/ Timezone
Duration.
Recently, a new data type called the Binary data type has also been noticed.
Power Bi Course :
https://www.youtube.com/watch?v=_zioodkDZ4U

Cyber Security is the practice of Protecting computers, mobile devices, Servers, electronic Systems, networks, and data from malicious attacks. It’s also known as Information Security (INFOSEC) or Information Assurance (IA), System Security. Cyber Security is important because the government, Corporate, medical organizations collect, military, financial, process, and store the unprecedented amount of data on a computer and other property, personal information, or exposure could have negative consequences.

Cyber Security proper began in 1972 with a research project on ARPANET (The Advanced Research Projects Agency Network), a precursor to the internet. ARPANET developed protocols for remote computer networking. Example – If we shop from any online shopping website and shared information like email id, address, and credit card details as well as saved on that website to enable a faster and hassle-free shopping experience then the required information is stored in server one day we receive an email which state that the eligibility for a special discount voucher from XXXXX (hacker use famous website Name like Flipkart, Amazon etc.) website in order to receive the coupon code, and we will be asked to fill the details then we will use saved card account credentials. Then our data will be shared because we think it was just an account for the verification step then they can wipe a substantial amount of money from our account.

That is why Cyber Security provides Service as a Security Gate-Way to make information more Secure, in today’s time hackers are advance we can’t surely say the data store in my Devices is safe or not by outside threats. With Cybercrime increasing at a rapid pace, it’s crucial to have Cyber Security in place of personal life and our Business.
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Cyber Security Course :
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Digital Marketing Managers use various channels to reach customers, build their brands awareness and promote products. They wear many hats, making them creative strategists with deep knowledge of digital media planning strategies.
A successful Digital Marketing Manager needs to create and upload copy to websites and other platforms, optimize images for their website and understand how marketing impacts its goals. They also have the responsibility of providing accurate reports on the Return On Investment of their marketing campaigns to demonstrate the effectiveness of specific campaigns.
A Digital Marketing Manager is responsible for developing and implementing strategies that promote a company or brand's products. They manage various channels like social media networks, Google Ads, website content and email marketing to deliver a persuasive and cohesive marketing message to their audience. Learn Digital Marketing Course :
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The data analytics market is booming. According to IDC analysts, businesses were estimated to have spent a whopping $215 billion in 2021 on big data and business analytics solutions, which was a 10% increase over 2020. The demand for data analytics professionals is also exploding. U.S. Bureau of Labor Statistics researchers project strong growth (31%) in the field of data science through 2030. Nearly all (90%) corporations are predicted to value information as a "critical enterprise asset and analytics as an essential competency" this year.
Artificial intelligence (AI), automation and machine learning are changing the game for organizations everywhere. AI is making rapid progress especially in the field of data analytics, in which it not only augments human capabilities but also helps derive better business value. The pandemic and remote work has significantly increased opportunities to track and measure data, spawning a new data-driven culture in organizations. This data culture is fueling investments in AI-based analytics. Learn Data Analytics Course [
https://www.youtube.com/watch?v=tgMTfUCUm-U] and get to know more about data analytics.

AI has wide applications to drive business value. Some examples include increasing sales by forecasting demand and ensuring that warehouse stocking is appropriate, improving customer satisfaction by reducing delivery time and boosting operational efficiency by automating processes that would otherwise require a human.