Introduction
In today’s fast-paced world, learning AI has become the norm. Regardless of which industry you are in, it is vital to at least know the basics of AI and its potential in transforming operations. As a revolutionary technology that transforms the way people communicate and handle data, there are various types of AI.
- ● Artificial Narrow Intelligence (ANI)- Designed to perform a single task, like voice recognition or recommendations on streaming services.
- ● Artificial General Intelligence (AGI)- This type of AI understands, learns, and implements knowledge across a wide range of tasks at a human level.
- ● Generative AI (Gen AI): A subset of AI that can generate content like images, audio, video, and text based on patterns learned from existing data.
- ● Artificial Super Intelligence (ASI)- This is a speculative model which is likely to be the future of AI. ASI is a future scenario where AI surpasses human intelligence.
Create a Learning Path
Without an aim or primary objective in mind, you cannot start your journey into learning AI. Therefore, it is vital for you to have a clear-cut goal and ambition for what you need to achieve from learning AI. For instance, if you are a data scientist, you may have an objective of learning generative AI to implement your learning in future projects and boost career growth. Also, you need to ask yourself whether you are looking to pursue a career in data science or to upskill yourself in your organization. Once you are clear with your goal, you need to craft a learning plan based on your objectives. Your plan should cover the following:
- ● A tentative timeline for completing the learning
- ● Goals to build your skills and the programs and resources for attaining those skills
- ● Assignments/projects that you need to take up to strengthen your skills
Understand the Pre-requisites
After creating a plan, you need to learn certain basic concepts before delving into learning AI. As it is a broad field with several subtopics, you need to understand the basics so that you can pave a clear path and explore different areas to find your passion.
As AI is heavily dependent on mathematical concepts, particularly fields like machine learning and deep learning, you need to have a basic understanding of algebra, calculus, and probability. Also, since AI is widely used in data science-related applications, it is essential for you to know statistics, regression, and data interpolation. Most importantly, if you are keen on pursuing a career in AI, you need to have the willingness to learn new concepts as AI is constantly adapting and evolving in various ways.
Familiarize yourself with AI Tools and Programming Languages
Although you need not be an expert in coding, you need to know the basics of Python and R programming languages as they are widely used in the AI space. Python is known for its flexibility, availability, and simplicity in implementing frameworks. Here are some essential libraries that you need to know.
- ● Python: A versatile high-level programming language that is known for its readability and versatility. It has a user-friendly syntax and a plethora of libraries and frameworks for AI and data science applications.
- ● Pandas: A Python library that provides means for data analysis, data cleaning, transformation, and statistical analysis. Pandas works best with unlabelled and messy data, making it ideal for processing datasets.
- ● NumPy or Numerical Python: This library helps in supporting multidimensional arrays with mathematical functions to operate on the arrays.
- ● Scikit-Learn: Useful for data mining and machine learning, this open source library is built on NumPy, SciPy, and matplotib.
- ● PyCaret: A powerful library that is used in deploying artificial intelligence models to enable users to explore, train, and compare machine learning algorithms with just a few lines of code.
Learn Generative AI
Gen AI has endless potential for growth and application regardless of the industry. Due to the immense scope of generative AI, learning this technology will take you miles ahead in your career. The core advantage of knowing gen AI is that it helps in analyzing and understanding the structure of training data and generating an equivalent input which is similar in style and structure to the original data. This unique aspect makes gen AI stand out from other AI types as it has a plethora of uses in today’s competitive landscape and fast paced world. There is a phenomenal growth potential of gen AI in today’s landscape.
Once you have understood the core aspects of gen AI and its applications, you need to learn how to use different AI-powered tools and how they can be integrated into different applications and operations. The best way to put your learning to the test is by performing a project (Capstone project). Choose a topic which is suitable for your line of expertise and work on developing an AI model that can help in optimizing the operation. This will help you to get a strong understanding of AI and showcase your skills and expertise to hiring managers.
Take up the Gen AI Course
Whether you are an established professional or a fresher in data science, IT, or other related fields, the best way to learn AI is by enrolling on a course to learn AI. Look no further than Eduinx, a renowned e-learning institute in the edtech space. At Eduinx, we help you gain an in-depth understanding of generative AI and data science concepts through a holistic experience. Our mentors have over a decade of experience in AI implementation across industries and will help you by crafting an appropriate learning plan that fits your career goals.
Mentors at Eduinx will also guide you in performing capstone projects that help you implement your understanding in a clear and concise manner. Our team will help you stay prepared to face the market and help you choose from different generative AI jobs. Eduinx’s generative AI course will help boost your career by learning the complex AI principles and implementing them in a holistic way. Learn more about Eduinx’s courses here and become an expert gen AI professional.
