Overview of Artificial Intelligence

Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for people with disabilities that enables them to function normally) works? If yes, this Artificial Intelligence tutorial will give you an introduction to AI right from the basics.

In this Artificial Intelligence tutorial, we shall be covering Machine Learning, Deep Learning, neural networks, real-life applications of Artificial Intelligence, Python and various packages available in it, TensorFlow, Keras, multilayer perceptron, convolution neural networks, recurrent neural networks, long short-term memory, OpenCV, and much more.

Watch this video on Artificial Intelligence Tutorial for Beginners:

Artificial Intelligence Tutorial – Learn Artificial Intelligence from Experts Overview of Artificial Intelligence Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for

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What are the goals of Artificial Intelligence?

Creativity and ideas never end as they are limitless. Likewise, there are a lot more things to create, improve, implement, and invent in the field of Artificial Intelligence. Evidently, AI is far from reaching its saturation level of creating new things.

In short, here are the goals of Artificial Intelligence:

  • Create machines that can replicate human beings
  • Improve machine efficiency and accuracy
  • Develop tools to help people solve real-world problems, e.g., robotics for people with disabilities, auto-driving cars to avoid accidents caused by human error, etc.

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What is Artificial Intelligence?

Artificial Intelligence is the intelligence that machines demonstrate. It allows us to create machines that can perform multiple tasks and solve real problems without error. As a matter of fact, AI can improve efficiency and productivity by automating repetitive tasks. Additionally, it can create an immersive, responsive experience and understand human emotions.

This Artificial Intelligence tutorial will teach you all the techniques that you will have to implement in real life.

Applications of Artificial Intelligence

Artificial intelligence machines have the ability to make decisions and when exposed to large amounts of real-world data, they try to learn and improve themselves. To illustrate this, here are some practical applications of artificial intelligence:

  • Self-Driving Cars: Tesla’s famous self-driving cars is a magnificent real-life application of Artificial Intelligence. These cars have in-built IoT sensors for image recognition, forehead collision, spot monitoring, and many more complex mechanisms that allow them to navigate and work in real life.

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  • Google Translate: Google Translate is another great application of Artificial Intelligence. It helps us translate sentences formed in one language to another. It can also translate the entire text on websites, which is possible only because of Artificial Intelligence.

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  • Amazon’s Alexa: Alexa includes a speech recognition system that listens to our voice commands and gives answers. It recognizes our voice and then interprets it as a series of commands and returns the results to us. It uses AVS (Alexa Voice Service), which Amazon provides for free of cost.

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  • Google Maps: Today, without Google Maps, it is impossible to survive in the city. With Google Maps, we can travel from one place to another without any difficulty. All we have to do is open Google Maps and enter our location. Then, its navigation will lead us with the most optimized path to our destination. This is also one of the wonderful applications of artificial intelligence.

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Now, in this Artificial Intelligence tutorial, we will head on to the subsets of Artificial Intelligence.

Subsets of Artificial Intelligence

Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence: Machine Learning and Deep Learning.

Machine Learning

Machine learning is a branch of artificial intelligence in which a program or machine uses a set of algorithms to find patterns in the dataset(s). Above all, we don’t have to write individual instruction for every action. As machine learning models capture more and more data, they become smarter and self-improving.

Further, Machine Learning can be sub-categorized into three subsets:

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning

Now, moving ahead with this Artificial Intelligence tutorial, we will look at some applications of Machine Learning.

Watch this video on Tensorflow Tutorial for Beginners:

Artificial Intelligence Tutorial – Learn Artificial Intelligence from Experts Overview of Artificial Intelligence Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for

Applications of Machine Learning

  • Amazon Recommendation System: Amazon’s recommendation system works on Machine Learning algorithms. In order to find patterns and similarities in our search, it leverages its unique design to recommend similar search results.
  • Machine Learning used in Fraud Detection: Today, as the number of fraud cases increases, machine learning plays an important role in solving this problem. Due to its unique design, fraud detection algorithms help distinguish between authorized and fraudulent transactions.
  • Machine Learning used in Social Media: Facebook uses Machine Learning algorithms to suggest friends to us with its ‘People You May Know‘ feature. Also, it uses Machine Learning for face detection, i.e., recognizing faces in a group photo. Isn’t it a wonderful application of Machine Learning?

Since Artificial Intelligence and Machine Learning make our lives better, it is very satisfying to learn these. This Artificial Intelligence tutorial will teach you everything you need to know about the basics of Artificial Intelligence.
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Deep Learning

Further development of machine learning has led to a different sub-category, i.e., Deep Learning. Deep Learning makes use of artificial neural networks that consist of layers of networks working on different parameters to give the desired output.

If you are preparing for an Artificial Intelligence job interview, go through this top Artificial Intelligence Interview Questions and Answers!

Applications of Deep Learning

  • Predicting Earthquakes: This is one of the most important applications where the field of Deep Learning is playing a key role. Unquestionably, time is an important factor when it comes to the prediction of earthquakes. Fortunately, with Deep learning, we are now able to boost the computation time by almost 50,000 percent.
  • Adding sounds to silent movies: This is a fascinating application for deep learning. The idea behind this deep learning model is to produce sounds that exactly match with a silent video. The DL models are provided with thousands of videos and audio files with which they try to learn by separating video frames for different categories of sounds. Thus, when a new video is given to them for adding audio to it, they use this learning for further prediction. Also, they use the convolutional neural networks, recurrent neural networks, and LSTM (long short-term memory) for achieving for their successful implementation.
  • Netflix Recommendation System: People using Netflix are familiar with its recommendation system. It uses Deep Learning for recording the responses of different kinds of audiences. The Deep Learning models are designed in such a way that they record the history of watching, time of watching, and our show preferences to recommend shows. It really saves a lot of human effort.

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What makes Artificial Intelligence so popular?

Artificial Intelligence is about incorporating the human abilities to a machine by designing algorithms such that these algorithms involve self-learning and provide the machine with the ability to think like the way humans do, by which the machine would be able to solve problems without explicit human inputs. However, a lot of creativity and computation is required to make it a successful creation.

Artificial Intelligence is gaining immense popularity because it has a lot of things to explore and create around the world. The applications of Artificial Intelligence that we come across in our day-to-day life are just a small demonstration of AI’s wonder-struck start.

Interested in learning Artificial Intelligence? Learn more from this Artificial Intelligence Course in New York!

Recommended Audience

This Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way, and it is meant for the beginners and for the professionals to help them understand basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you master AI with which you will be able to take yourself to a higher level of expertise for implementing Artificial Intelligence concepts in real life.

Check out our Artificial Intelligence Tutorial video:

Artificial Intelligence Tutorial – Learn Artificial Intelligence from Experts Overview of Artificial Intelligence Today, a few applications of artificial intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for

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Prerequisites

Before going through this Artificial Intelligence tutorial, you should have a fundamental knowledge of the field of Information Technology, along with being familiar with computers, the Internet, and basic working knowledge on data. Such basics will help you understand the AI concepts better and will move you faster on the learning path.

This AI tutorial, further it its pages, covers the introduction to AI, the history, goals, and the applications of AI, AI vs ML vs DL, various data science packages, artificial neural networks, back-propagation algorithm, multilayer perceptron, the problems of overfitting and underfitting, convolution neural networks, recurrent neural networks, long short-term memory, various Machine Learning concepts, and OpenCV.

If you have any technical doubts or queries related to AI, post the same on Intellipaat’s AI Community!

Frequently Asked Questions

What are the basics of AI?

AI is basically the study of computer science that focuses on devising machines or developing software that exhibits human behavior. Primary goals of Artificial Intelligence include reasoning, deduction, knowledge representation, planning, learning, Natural Language Processing (NLP), and the ability to manipulate objects.

What are AI techniques?

AI techniques are nothing but organized methods to leverage knowledge for performing AI operations such as correcting errors. These techniques are models that are derived from advanced forms of statistical or mathematical data sets.

Where can I learn AI?

AI can be a tricky subject to master, but this tutorial is a beginner’s guide. It covers the concepts of AI from scratch and therefore is really handy for anyone who wants to learn AI. If you want to learn AI in an interactive manner, here’s the link to our free and comprehensive AI video tutorial on YouTube.

Also, you can enroll with us for our online AI course to learn AI from industry experts. Our course features numerous hands-on assignments and industry-relevant projects,

How do I start learning AI?

To start learning AI, the first thing you need to do is understand what matters the most in AI. You need to figure out a field in AI you are interested in and solve the problem which is associated with it. First find a quick solution for the problem, and then slowly and steadily improve the initial solution. As per experts, this approach is best poised for learning AI and building a career around it.

How is Python used in AI?

Owing to its intuitive syntax, basic control flow, and data structures, Python is useful for prototyping AI algorithms. Today, almost all people who work around AI are trained Python experts.

What is AI coded in?

Based on varied needs in the development and designing of different software, AI codes are written in Java, Python, Lisp, Prolog, and C++. However, Python is used for AI coding predominantly.

Is Artificial Intelligence a good career?

Ever since its birth, AI has been growing at an exponential rate. Numerous companies today rely on the capabilities of AI to ease their business operations and foster improvised products. Owing to this trend, there is a drastic surge in demand for AI professionals throughout the globe. Therefore, AI is a great career option to opt for.

Are data scientists in demand?

Considering the recent expansion of AI and ML across all verticals, the demand for data scientists is at an all-time high. Not only are employers willing to hire data scientists (be it experienced or freshers), but also they are offering huge compensation for AI-based job roles. It is definitely a great era for you to become a data scientist.

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

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