Artificial Intelligence Tutorial
The free Artificial Intelligence tutorial is an introduction to AI that gives you knowledge on basics of AI, Machine Learning, Deep Learning, various application areas of AI, Python, various packages available in it, Tensorflow, Keras, Neural networks, Multilayer perceptron, Convolution neural networks, Recurrent neural networks, Long short term memory and OpenCV.
What are the Goals of AI?
- To create machines which can do better performance than the previous version.
- To add new features which human possess.
But what is Artificial Intelligence?
Artificial Intelligence is all around us. Artificial Intelligence creates a higher degree of efficiency and productivity by automating the repetitive task and creating immersive and responsive experience and understanding human sentiments and even emotions.
This tutorial will help you master AI by taking you through a step-by-step approach while learning AI and Machine learning concepts.
So what makes AI so hot?
AI is able to think like the way we humans do, is able to solve problems without the explicit inputs form us, can deal with abstract concepts like ideas, and this technology is truly at attempt to understand randomness and creativity.
This tutorial has been prepared for the beginners as well as for the professionals to help them in understanding basic-to-advanced concepts related to AI. This tutorial will help you in understanding about AI from where you will be able to take yourself to a higher level of expertise.
AI Tutorial Video
Before going through this tutorial you should have fundamental knowledge of information technologies such as Computers, Internet and basic working knowledge on Data. Such basic concepts will help you in understanding the AI concepts in a better way and will move you faster on the learning track.
This AI Tutorial covers Introduction of AI, History, Goals, Application areas, AI vs ML vs DL, Python and its installation, various data science packages, installation of python and keras, tensorflow objects, Artificial Neural networks, Multilayer perceptron, problem of overfitting, underfitting, Convolution neural networks, Recurrent neural networks, Long short term memory, OpenCV and GAN.