Artificial Intelligence: Skill Guide 2020


The use of technology is increasing day after day and so are the methods being advanced to reach the increasing demand. Today, we have come to a point where digital channels are becoming the norm, providing the brain with all the senses and limbs it needs. A typical Artificial Intelligence perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

Artificial Intelligence has the potential to vastly change the way that humans interact with the digital world and in the near future, its impact is likely to grow further. One such example of human-like task automation techniques is the Robotic Process Automation (RPA). Hence, Artificial Intelligence is a field with an assured future for growth.

Here are a few skills that one needs to adapt to, to survive in the field of AI:

1. Machine learning

Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and develop from past cases without being explicitly programmed. These algorithms focus on the development of computer programs that can access data and use it to learn themselves. The machine learning algorithms use Computer Science and Statistics to predict logical outputs. There are three major areas of ML. They are:

  • Supervised Learning: In supervised learning, training datasets are given to the system. Supervised learning algorithms analyze the data and produce an inferred function. The correct solution so produced can be used for mapping new patterns.
  • Unsupervised Learning: In unsupervised learning, the data given to the system is unclustered. The goal of this kind of learning is to have the machine learn on its own without any supervision. These algorithms are much harder as the correct solution to any problem is not provided and the algorithm itself finds various patterns in the data.
  • Reinforcement Learning: Reinforcement Learning is a type of machine learning algorithm that enables software agents and machines to automatically determine the ideal behavior in a specific context.

Machine Learning Use Cases:

  • Rapid Analysis Prediction and Processing
  • Massive Data Consumption from Unlimited Sources
  • Financial analysis
  • Detect network intrusions

Applications of Machine Learning:

  • Machine learning is one of the important aspects of cybersecurity.
  • It can be used for online fraud detection.
  • Machine learning can be used for medical diagnosis

2. Python

Python is a programming language based on Object-Oriented Programming. It is a very useful and robust programming language which focuses on RAD(Rapid Application Development). Python’s ever-changing libraries make it an ideal choice for developers who want to work on any project. These projects can be mobile applications, web applications, or Artificial Intelligence. Python has very few keywords, a simple structure, and a clearly defined syntax. The code is clearly defined, easy to maintain and can be easily integrated with other programming languages like C, C++, and Java. Python also supports automatic garbage collection. It can be run on a wide variety of hardware platforms and has the same interface on all platforms.

3. Java

Java is an object-oriented general-purpose programming language which is specifically designed to have few implementation dependencies as possible. It works on various platforms and is one of the most popular programming languages in the world. It derives a significant part of its syntax from C and C++. Debugging is easy in this programming language. It is fast, powerful and secure. Java is portable as it is architecture-neutral and has no implementation dependent aspects of the specification. Multithreading feature in Java makes it possible to write programs that can perform various tasks simultaneously. It makes an effort to eliminate error-prone situations by focusing mainly on compile time error checking. The authentication methods are based on public key encryption.

4. R

R is a multi-paradigm language that can be defined as a dynamically-typed, scripting, procedural, and interpreted language. It can also support a type of object-oriented programming but is less known for that as compared to Python programming language. It is considered as a statistical software and is very specialized and well-suited for statistics, data analysis, and data visualization. It provides graphical tools for data analysis. It also has effective storage and data handling facility. It runs on all platforms and can be easily ported to another without any problems. It can be used to monitor user experience on social media.

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Artificial intelligence is a growing field in today’s industry. Employer demand for AI positions has increased in the past years and many titles like machine learning engineer, data scientist are few among the many in-demand AI jobs.

AI skills automate repetitive learning and discovery through data. With the help of artificial intelligence, one can achieve incredible accuracy through neural networks. The development process of AI-based projects is becoming easier with the above-mentioned skills.


Nice blogs . Thanks a lot

- Nirav

very helpful and updated to latest technology. Thanks for helping us!!

- Yogesh

Nice Blog...

- Sandip

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