Overview of Artificial Intelligence

This Artificial Intelligence tutorial gives you an introduction to AI right from the basics. We shall be covering Machine Learning, Deep Learning and 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.

Watch this Artificial Intelligence Tutorial for Beginners video


Become Master of Artificial Intelligence by going through this online Artificial Intelligence course in London.

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 Artificial Intelligence 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.

Artificial Intelligence Course

Audience

This Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way and is meant for the beginners as well as for the professionals to help them in understanding basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you in understanding about AI from where you will be able to take yourself to a higher level of expertise when you learn Artificial Intelligence from this tutorial.

AI Tutorial Video

Interested in learning Artificial Intelligence? Click here to learn more in this Artificial Intelligence Course in New York!

Prerequisites

Before going through this tutorial you should have a 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. If you will to preparing for Artificial Intelligence job please go through this  Top Artificial Intelligence Interview Questions And Answers.

Artificial Intelligence Training

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.
If you have any technical doubts & queries related to Artificial Intelligence, post the same on Intellipaat Community.

Table of Contents

Introduction to AI

What is AI?

Artificial Intelligence(AI) is everywhere around us, say voice recognition software in your mobile phone or navigations in car. Even Google uses Artificial Intelligence for its search engine. Netflix, amazon prime movie suggestions are based on Artificial Intelligence. Interacting with Siri, Alexa, Google assistant is also a form of Artificial Intelligence. Artificial Intelligence is a broad area of Read More

AI vs ML vs DL

Understanding Artificial Intelligence vs Machine Learning vs Deep Learning

We know that Earth is surrounded by atmosphere, and it comprises layers of atmosphere. The layer which is suitable for human beings to survive is troposphere. There are three more layers which we would not usually discuss about. However, the key focus is that the atmosphere is the umbrella term for Read More

Artificial Neural Networks

Artificial Neural networks

ANN was developed considering the same as of our brain, same how our brain works was taken into account. It was inspired by the way neurons work, the major task is to process information. The architecture of neural network is similar to neurons. Frank Rosenblatt in 1958 invented ANN and built the machine learning algorithm. Learn more Read More

Multi Layer Perceptron

Math behind Neural networks 

The input layer is usually a vector, the neural network learns the pattern by learning the weights. The architecture, activation functions, layers in it, dropouts, weights of each epoch is saved in pickle file. There are also the biases stored. Learn in depth about Math behind Neural networks in this Artificial Intelligence Course. Watch this Artificial Intelligence Read More

Convolution Neural Network

What is CNN?

The neural nets exists and in addition to that an image is convoluted, converted in pixel level and studied, converted and a max pooling, this entire thing is known as convolution + pooling layers. A fully connected layers of flattened structure of numpy array and a hidden layer is then classified into various classes as binary or Read More

Recurrent Neural Network

Basic difference between Deep Neural Network, Convolution Neural Network and Recurrent Neural Network

In this tutorial we will see about deep learning with Recurrent Neural Network, architecture of RNN, comparison between NN & RNN, variants of RNN, applications of AE, Autoencoders - architecture and application. Deep neural networks Convolution neural networks Recurrent neural networks Provides lift for classification and forecasting Features Read More

Machine learning & OpenCV

Why save the model?

In this tutorial we are going to see about the machine learning flow from development to release phase, what is the need of saving a model and basics of OpenCV, GAN. Watch this Natural Language Processing (NLP) Tutorial for Beginners video [videothumb class="col-md-12" id="KVxIx8f_VpM" alt="Natural Language Processing (NLP) Tutorial" title="Natural Language Processing (NLP) Tutorial"] We need Read More

Back Propagation Algorithm

Watch this Introduction to Artificial Intelligence video

In an artificial neural network, the values of weights and biases are randomly initialized. Due to random initialization, the neural network probably has errors in giving the correct output. We need to reduce error values as much as possible. So, for reducing these error values, we need a mechanism which can compare the desired output of the neural network with Read More

Next

Recommended Videos