What Are The Prerequisites To Learn Artificial Intelligence

At the very core of learning artificial intelligence are subjects learnt at high-school and make excellent courses to get ready for a career in Artificial Intelligence.

• Computer Science Fundamentals.
• Data Structures.
• Algorithm Analysis.
• Calculus. 
• Discrete-Mathematics.
• Linear Algebra including matrices, vectors and derivates.
• Statistics and Probability.
• Python programming.

Applications that use AI:

As big data, AI and ML evolve, cyber threats and cyber-security are evolving to keep pace. The Gartner report explains that cybersecurity is the biggest spend today. Alongside the new developments in AI, cyber-security and ML there is an all-real possibility of hackers also using the same infection-detection technology and malware samples to advance the technology of cyber-threats.  The role of AI, ML and data analytics is to filter information for security professionals while giving them real-time information insights on any aberrations. With automatic detection taken care of and responses quickly following one can look at robust and rapid defence systems.

Some of the popular applications are:

• Automatic and self-driven automobiles.
• Facial recognition.
• Web-search engines.
• Robotics and industrial-use robots.
• Guidance of missiles.
• Detection of tumours and cancers.
• Computer games.
• Animation and its application.
• Machine Learning and very big data analytics.

How to acquire AI Knowledge:

Most market courses today from reputed institutes help understanding AI and its applications in resolving real-world issues in applications across diverse fields, technologies, languages and platforms. 

Here is what you can expect to learn at such courses:

• Building AI apps like games, self-driven cars, real-time issues etc.
• Understand the fundamentals and theory of AI.
• Building AI agents like search-engines, logic, constraint-satisfaction issues etc.
• ML algorithms.
• AI modelling and applications to robotics, language-processing and vision.
• Q and Deep-Q Learning
• Convolution Q-Learning
• Resolving AI issues with Python.

Non-transferable skills which are self-taught and cannot be acquired through these courses need to be fine-tuned, aligned and well-developed in a career in Artificial Intelligence and Machine Learning. The following traits are essential and work in tandem with technical skills.

1. Interpersonal team and communication skills.
2. Innovative ability to creatively solve problems.
3. Collaborative and contributive team skills
4. Ability to work in an environment filled with pressure, demands and ambiguities involved in the startup environment.
5. Dedication, discipline, integrity and hard work are required to evolve into a mature personality.
6. Remember good learners thrive where no standard operating procedures or interpretations are available.

Advantages of doing an Artificial Intelligence Course:

1. Acquire ace level AI coding skills

You get to learn AI code that is self-improving and built for a very wide range of applications. Most coding happens from scratch and under supervision of certified industry-drawn coders who help, supervise, guide and mentor you throughout the process. 

2. Downloadable Code-templates

All modules of a globally certified and well-designed curriculum means a Python code downloadable AI template for all projects you execute. The potential to use your code for various other applications by changing a few lines of code will make you familiar with coding line-by-line.

3. Tutorials that hone your intuition

Where other theoretical courses teach you concepts, these courses help understand the practical aspects of being able to apply your learning to resolve practical issues in AI. Intuitive coding is best honed through practice sessions, assignments, workshops and quizzes that help imbibe and inculcate the learning.

4. Apply knowledge to real-time AI solutions

As you progress through the modules you become more adept at integrating real-life applications and your learning. The able mentoring of industry-aces will help you get through applications with best-practices, techniques and tricks that come with years of industrial experience.

5. Course and learning support

The courses have full-time and part-time learning options with certification, interactive and virtual classes, peer support forums, post-course support, library access and such which help you learn, retain and use the knowledge skillfully.

Certification

The qualifying exams and certification are true measures of your ability to use your learning in practical situations. Besides being a feather in your cap they are pre-requisites for developer jobs.

In conclusion, if you plan to pursue a career in AI then do a course from a reputed institute like Imarticus Learning. The learning and certification gained here is industry-acceptable globally and is a true measure of being able to apply your knowledge practically.