Today, Artificial intelligence has achieved an undeniable success. If your work involves using the keyboard in any aspect, AI will completely change the way you work soon. According to the statistics, almost 97% of businesses expect positive outputs in their work with the help of AI. They can use Chat GPT or other AI tools to perform difficult or time-consuming tasks in minimum time. The tools of AI including Midjourney, Bard, and Chat GPT are significantly ushering Artificial Intelligence into the mainstream. This positive outcome has made the art and science platforms of AI comparatively more relevant to each other than ever before.
This article will be of assistance and great help if you are a machine learning engineer, AI researcher, data scientist, or an AI enthusiast.
What is AI?
AI is a branch of computer science that is about machines or models that can perform tasks that require human intelligence. The tasks include understanding a natural language, making decisions, learning from the experience, and recognizing the patterns. Artificial Intelligence is a broad field that has numerous subfields having their perspectives and roles.
A Complete Guide to Learning AI Step by Step:
AI has gained importance as a standalone major in a few universities as a new field. However, most of the AI experts came from related stem studies of data science, computer science, statistics, or even mathematics.
On the other hand, you can pursue this field on a more traditional path by obtaining a degree in any of these fields related to AI. The requirements of your program may vary according to the degree you selected. Recency and dynamic nature, online education of AI, and independent learning are the main ways to start a career. To learn AI on your own, but aren’t sure where to begin, follow the given steps.
Step#1: Get Fundamental Theoretical knowledge:
To learn about AI on your own, one should first learn how to apply and understand the complex concepts of AI. You need a perfect theoretical foundation in math, statistics, and data.
Mathematics:
It is not compulsory to be a mathematician to learn AI, deep learning, and machine learning but still, you need to learn about some mathematics principles. Understanding the concepts of linear algebra and calculus will help you to detect, and fix errors in AI models and also in developing a new algorithm. So, if you plan to learn Artificial Intelligence, a general idea about mathematic concepts will be enough.
Statistics:
Learning Statistics will help you with analyzing, interpreting, and visualizing the data. This study is pivotal for evaluating the performance of an AI model. Moreover, many AI techniques require the learning of statistical principles like clustering, classification, or regression. That’s why, after learning the basic concepts of mathematics, you should learn statistics concepts.
Probability:
It gives the framework for making decisions even in uncertainty that can be considered the basis of AI. Specifically, the AI analyzes the situation and comes up with a probable outcome. AI learns by experience and updates probabilities after getting new information. Similarly, Naïve Bayes depends on probability principles.
Data Related Skills:
As AI is trained on data, it is an essential part of learning. There are various data-related aspects that you need to learn. However, the most essential ones are given in the description.
- Data Collection
- Data Cleaning and Preprocessing
- Database Management
- Data Wrangling
Step#2: Programming:
Learning programming plays a significant role in learning AI. Learning programming plays a vital role in taking you on the journey of functional algorithms from basic theoretical concepts. One cannot understand, or generate an AI model without programming. However, there are AI models available that can write codes for you way faster than an ordinary programmer, but still, you will need a proper understanding of programming to move forward in your journey of learning AI.
The languages of Python and R are prevalent with their robust libraries that are designed for Artificial Learning, Machine Learning, and Deep Learning. So, your next step will be the learning fundamentals of R and Python programming and after that, you will move to further learn about frameworks that are specially optimized for AI.
Step#3: Leverage Machine Learning:
According to a recent survey, almost all contemporary AI solutions are generated by Machine learning. Thus, Machine learning also plays a vital role for AI whether just for research purposes or applied role in AI. The knowledge you will gain in mathematics, statistics, probability, programming, and probability works as a solid base for the study of machine learning. After learning ML, you will be able to execute the end-to-end ML processes starting from defining a certain problem to model deployment in detail.
Step#4: Understand Deep Learning:
Deep learning has also its own vital role in the learning of AI as it facilitates you with a more advanced understanding of the models. After acquiring all the above knowledge, understanding the logic behind deep neural networks will be interesting. In deep learning, you will first learn basic technical skills and then move to learn the terminology behind it.
Step#5: Specialization in the Subfield:
After learning the fundamental knowledge of AI, you will move to learn about the specialization in your field, for example in natural language processing, computer vision, robotics, etc. The are a plethora of options available for you to learn in AI and each field will have its requirements of skills and qualifications.
Step#6: Soft Skills of AI:
No matter how advanced AI has become now, it still cannot comprehend the intricacies of real-life situations which includes adaptability, strategic thinking, and intuition. These soft skills will shape the work of many AI models effectively. As the AI models need to communicate more meticulously with stockholders, and should be eligible enough to make even better decisions leverage AI, and help in achieving in company’s goals. These soft skills can be:
- Data Literacy
- Data Strategy
- Data-Driven Business Growth
- Machine Learning Deep Dive
- Communication and Presentation Skills for Analysts and Managers
- Product Management for AI and Data Science
