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Step-by-step guide to become an AI expert

Experts claim that artificial intelligence (AI) will shape people's lives within the next ten years. Ultimately AI can be a game-changer to increase human effectiveness and remove worldly tasks.

"AI is going to change more than anything in the world in the history of mankind. More than electricity. ”- AI expert Dr. Kai-fu Lee, 2018

Artificial intelligence is being used by almost all major industries - from education to healthcare to retail and e-commerce, public relations (PR), small businesses, recruitment and services, and manufacturing.

By 2030, AI is said to contribute USD 15 trillion to the world economy, Source PwC.

As AI becomes the central force to transform society, this technology is likely to distort our lives in many ways.

Step-by-step guide to become an AI expert

But are you ready to adopt change?

Becoming an AI expert is not a cakewalk.

Artificial intelligence is becoming a part of everyday life, and you have just started.

There are some great ways to start an online learning platform, but you cannot master AI without planning. You can come across a plethora of online platforms offering different learning avenues - to ensure that the blueprint is embedded in your brain regardless of the path you choose.

Still confused?

We have given you just the right formulas to get you started.

You can follow the right step-by-step guide to build a career and become one of the world's outstanding AI engineers.

let's get started -

Step 0: Basics of R and Python Programming

Many programming languages ​​implement different concepts, algorithmic flow structures and patterns, and so on. However, the goal is not to become a master in a specific language, but rather to gain extensive expertise in such structures and concepts. Once this is completed, it becomes easy for you to choose any programming language you wish to learn.

The two major popular programming languages ​​for AI are Python and R. But if you should choose one of these two, I should say that it's going to be tough. Both are open-source and are independent, developed in the 1900s. R works great for statistical analysis while Python is perfect as a general purpose programming language. In short, R and Python are ideal for AI and machine learning aspirations.

Step 1: Statistics (Descriptive and Unordered)

Descriptive statistics are ideal when a polite set of data is described to help describe or summarize data in a meaningful way. For example, if we have 100 student course outcomes, we may be able to summarize the overall performance of those students. This group of students is defined as a sample. Descriptive statistics can help us achieve that.

Whereas the estimated figures take data from the sample and estimate the population from where this sample was obtained. The goal of estimation statistics is to end with the results taken from the sample. Aspiring AI professionals should learn the basics of descriptive and residual statistics.

Step 2: Data Cleaning, Exploration and Preparation

One of the key features that define an exceptional AI expert from a general AI professional is to analyze the quality of data cleansing that occurs on the data gathered. The more time you spend cleaning it, the better time you spend. Since this process is time consuming, it is easy to find a way around the data cleaning process and to create a proper structure.

Step 3: Presenting Your First Step to Artificial Intelligence

AI is a technology that describes the way humans think about computers. Typically, building a machine that can perform tasks that usually require human intelligence. At its core, the main purpose of AI is to improve the functions of computer-related human intelligence - logic, problem solving and learning.

AI experts are called the rock stars of the engineering world and pursuing a career in artificial intelligence is a good career option. Not to mention, AI is called the next technology revolution, and joining the bandwagon is the right thing to do today.

To grab an excellent job as AI engineers, you need skills such as mathematics, algorithms, probability and statistics, programming in R and Python, commands on Unix devices, proficient in distributed computing, and advanced signal processing techniques.

Step 4: Get AI concepts in depth

To gain an in-depth knowledge of AI concepts and validate your learning, earning an AI certification is the best option. It helps you understand the concepts of AI, machine learning, natural language processing and deep learning.

In addition, you need to explore your knowledge about Python libraries for AI and machine learning. Some of the names include Keres, Tensorflow, Theano, Pytor, Pandas and Skitit-learning.

These libraries come in handy when developers and programmers need to perform complex tasks without the need to rewrite the code again. One of the reasons for the popularity of AI and machine learning in the last few years is traction.

With AI certification, you excel in artificial intelligence practices and strategies that enable you to deliver better results for the organization. In addition, many credential organizations today offer digital badges, earning AI certification. It increases your profile visibility through social sharing and attracts the attention of prospective employers who need a lot to win over strong competition.

Step 5: Win a chess contest

Kaggle is one of the best online repositories where you can start working with real-time projects, work with some of the best AI professionals, and build your first AI project. Now you can start your AI journey by winning AI competitions.

The future of AI is right at your doorstep, all you need to do is embrace technology!


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