- Please note that this course has the following prerequisites which must be completed before it can be accessed
- Information Security Diploma Training and Certification
About This Course
Artificial Intelligence is the hottest technology in this modern era. By using Artificial Intelligence we make machines more intelligent as humans. As humans learn from past experiences and improve in the future, we make machines as they solve their own problems. There are many examples of artificial intelligence applications that we use in daily life such as, Google Voice Recognition, Apple Siri, Amazon Alexa, Facebook friends recommendation, YouTube Recommendation, self-driving car, Robotics science, health care, and space science. Sophia Humanoid Robot is a living example of artificial intelligence and machine learning.
If you want to make a career in Artificial Intelligence and Machine Learning fields then should know some programming language and the most recommended programming language is Python. Python has the powerful library to implement in machine learning and deep learning models to solve real worlds problems. There are two applications of Artificial Intelligence as Machine learning and Deep learning. Machine learning is an application of artificial intelligence that is used to train the machine by using machine learning models for specific problems. They test the models for the specific problem and improve the models for future use. Deep Learning is working with human neurons and artificial neuronal networks that’s how the human brain works with these capabilities included in machines.
Types of learning in Machine learning:-
1. Supervised Learning:- Supervised machine learning needs a supervisor(Teacher) to train the models using labeled Training Dataset which means while training the models we give input and output to the dataset. It’s used to solve two types of problems Regression and Classification.
2. Unsupervised Learning:- In unsupervised machine learning we don’t need a supervisor(Teacher) to train the models. It uses unlabelled data to train the models. Unsupervised learning models are used to solve clustering and association rule learning problems
3. Reinforcement Learning:- Reinforcement Learning is a type of machine learning technique in which an agent learns from the environment by performing the action and seeing the result of actions.
4. Semi-supervised Learning:- Semi-supervised learning is an approach to machine learning that uses labeled and unlabeled data to train the model.
There are many professional opportunities for Artificial Intelligence and Machine Learning Experts such as Artificial Intelligence Expert, Data Scientist, Machine Learning Engineer’s, Data Analytic, Nature Language Processing Expert, and more.
- Does not assume any prior knowledge of Artificial Intelligence
- Bring your business and managerial experience
- The course will help you do the rest
- Business Analyst