ARTIFICIAL INTELLIGENCE (AI) & MACHINE LEARNING (ML)
What is Artificial Intelligence?
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence. In simple, AI’s goal is to make computers/computer programs smart enough to imitate the human mind behaviour. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering
AI services can be classified into Vertical and Horizontal AI
What is Vertical AI?
These are services focus on the single job, whether that’s scheduling meeting, automating repetitive work, etc. Vertical AI Bots performs just one job for you and do it so well.
What is Horizontal AI?
These services are designed that they are able to handle multiple tasks. There is no single job to be done. Cortana, Siri and Alexa are some of the examples of Horizontal AI.
What is Machine Learning?
Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again.
ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. The ML algorithms use Computer Science and Statistics to predict rational outputs.
There are 3 major areas of ML
Supervised Learning
In supervised learning, training datasets are provided to the system. Credit card fraud detection is one of the examples of Supervised Learning algorithm.
Unsupervised Learning
Unsupervised Learning algorithms are much harder. Here the goal is to have the machine learn on its own without any supervision. The algorithm itself finds the patterns in the data. One of the examples is Facebook friend request suggestion mechanism.
Reinforcement Learning
This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context. This technique selects the action that would give expected output efficiently and rapidly. One of the examples is Ad recommendation system.
How are AI and ML related?
While AI and machine learning are very closely connected, Machine learning is considered a subset of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own. Artificial Intelligence is a broader umbrella under which Machine Learning comes.
The Future of Artificial Intelligence & Machine Learning
ML will be an integral part of all AI systems, there is a strong possibility of this technology being offered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS). It will continue to act as a technological innovator in the coming years.
Common skills for Artificial and Machine Learning
Technical Skills
- Programming Languages: Python, R, Java, C++
- Linear Algebra, Calculus, Statistics: Matrices, Vectors, and Matrix Multiplication, Derivatives and Integrals
- Signal Processing Techniques: Signal Processing Algorithms like Wavelets, Shearlets, Curvelets, and Bandlets.
- Applied Math and Algorithms: Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential Equations, and Summations.
- Tools: R, Tableau, SAS, and work on real-world projects.
Non-Technical Skills
Communication, Domain Knowledge, Rapid Prototyping
Best Career Scopes in AI and ML engineering
Machine Learning Researcher, AI Engineer, Data Mining and Analysis, Machine Learning Engineer, Data Scientist, Business Intelligence (BI) Developer, Robotics engineer, Computer vision engineer, Big data engineer.
Opportunities
There are Many Opportunities in India and Abroad as well for this Skills.
