Experts claim artificial intelligence (AI) will shape the lives of people within the next ten years. Ultimately, AI can be a game changer in amplifying human effectiveness and remove mundane tasks.

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

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

Forward to 2030, AI is said to contribute USD 15 trillion to the world economy, source PwC.

As AI becomes the central force of transforming society, this technology is likely to pervade our lives in many ways.
But are you prepared to embrace the change?

Becoming an AI expert is indeed not a cakewalk.
 
Artificial intelligence is becoming a part of everyday life, and you’ve just gotten started.

Online learning platforms are some of the best ways to get started but you cannot simply become a master of AI without carving a plan. You might come across a plethora of online platforms offering different learning paths – despite the path you choose to ensure you have a blueprint embedded in your brain.

Still, confused?

We’ve got just the right formulae for you to get started.

You can dive right in and follow the step-by-step guide to yield a career and become one of the world’s outstanding AI engineers.

Let’s get started –

Step 0: Fundamentals of R and Python programming

Multiple programming languages implement different concepts, algorithmic flow structures, and paradigms, and so on. However, the goal is not about becoming a master in any specific language, but rather gain extensive expertise of such structure and concepts. Once these have been accomplished, it gets easier for you to pick up any programming languages you wish to learn.

Two major popular programming languages for AI are Python and R. But if you must pick from these two, let me say it is going to be tough. Both are open-source and they are free, 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 aspirants.

Step 1: Statistics (Descriptive and Inferential)

Descriptive statistics is ideal when describing a humongous set of data to help describe or summarize data in a meaningful manner. For instance, if we have the results of a group of 100 students’ coursework, we might be able to summarize the overall performance of those students. This group of students is defined as the sample. Descriptive statistics can help us achieve that.

While inferential statistics takes the data from the sample and makes an inference about the population from where this sample was obtained. The goal of inferential statistics is to conclude with the results taken from the sample. Aspiring AI professionals must learn the basics of descriptive and inferential statistics.

Step 2: Data cleaning, exploration, and preparation

One of the major characteristics that define an exceptional AI expert from an ordinary AI professional is by analyzing the quality of data cleaning that happens on the data gathered. The more amount of time you spend on cleaning the more you get better at acing it. Since this process is time-consuming, it gets easier to find a way and build a proper structure around the process of data cleaning.

Step 3: Introducing Your First Step to Artificial Intelligence

AI is a technology, an approach to make computers think the way humans think. Typically, building a machine that can perform tasks that usually require human intelligence. At its core, the main aim of AI is to improve the functions of a computer-related to human intelligence – reasoning, problem-solving, and learning.
 
AI experts are called the rock stars of the engineering world and earning a career in artificial intelligence is a sound career choice. Not to mention, AI is said to be the next technology revolution, and getting into the bandwagon is the perfect thing you’ll be doing today.

To grab an excellent job as AI engineers, you require skills like mathematics, algorithms, probability, and statistics, programming in R and Python, command over Unix tools, efficient in distributed computing, and advanced signal processing techniques.

Step 4: Gain in-depth AI concepts

Eventually, you will need to get familiar with Python libraries for AI and machine learning. Some of the names include Keras, TensorFlow, Theano, PyTorch, Pandas, and Scikit-learn.

These libraries come in handy when developers and programmers need to perform complex tasks without needing to rewrite the code over again. One of the reasons why AI and machine learning’s popularity has gained traction in the past few years.

Step 5: Win a Kaggle competition

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. You can now kick start your AI journey by winning AI competitions.

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