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Artificial Intelligence With Python:

Artificial Intelligence has been around for over half a century now and its advancements are growing at an exponential rate. The demand for AI is at its peak and if you wish to learn about Artificial Intelligence, youve landed at the right place. This blog on Artificial Intelligence With Python will help you understand all the concepts of AI with practical implementations in Python.

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The following topics are covered in this Artificial Intelligence With Python blog:

A lot of people have asked me, Which programming language is best for AI? or Why Python for AI?

Despite being a general purpose language, Python has made its way into the most complex technologies such as Artificial Intelligence, Machine Learning, Deep Learning, and so on.

Why has Python gained so much popularity in all these fields?

Here is a list of reasons why Python is the choice of language for every core Developer, Data Scientist, Machine Learning Engineer, etc:

Why Python For AI Artificial Intelligence With Python Edureka

If you wish to learn Python Programming in depth, here are a couple of links, do give these blogs a read:

Since this blog is all about Artificial Intelligence With Python, I will introduce you to the most effective and popular AI-based Python Libraries.

In addition to the above-mentioned libraries make sure you check out this Top 10 Python Libraries You Must Know In 2019 blog to get a more clear understanding.

Now that you know the important Python libraries that are used for implementing AI techniques, lets focus on Artificial Intelligence. In the next section, I will cover all the fundamental concepts of AI.

First, lets start by understanding the sudden demand for AI.

Since the emergence of AI in the 1950s, we have seen exponential growth in its potential.But if AI has been here for over half a century, why has it suddenly gained so much importance? Why are we talking about Artificial Intelligence now?

Demand For AI Artificial Intelligence With Python Edureka

The main reasons for the vast popularity of AI are:

More computing power: Implementing AI requires a lot of computing power since building AI models involve heavy computations and the use of complex neural networks. The invention of GPUs has made this possible. We can finally perform high-level computations and implement complex algorithms.

Data Generation: Over the past years, weve been generating an immeasurable amount of data. Such data needs to be analyzed and processed by using Machine Learning algorithms and other AI techniques.

More Effective Algorithms: In the past decade weve successfully managed to develop state of the art algorithms that involve the implementation of Deep Neural Networks.

Broad Investment: As tech giants such as Tesla, Netflix and Facebook started investing in Artificial Intelligence, it gained more popularity which led to an increase in the demand for AI-based systems.

The growth of Artificial Intelligence is exponential, it is also adding to the economy at an accelerated pace. So this is the right time for you to get into the field of Artificial Intelligence.

Check out these AI and Machine Learning courses by E & ICT Academy NIT Warangal to learn and build a career in Artificial Intelligence.

The term Artificial Intelligence was first coined decades ago in the year 1956 by John McCarthy at the Dartmouth conference. He defined AI as:

The science and engineering of making intelligent machines.

In other words, Artificial Intelligence is the science of getting machines to think and make decisions like humans.

In the recent past, AI has been able to accomplish this by creating machines and robots that have been used in a wide range of fields including healthcare, robotics, marketing, business analytics and many more.

Now lets discuss the different stages of Artificial Intelligence.

AI is structured along three evolutionary stages:

Types Of AI Artificial Intelligence With Python Edureka

Commonly known as weak AI, Artificial Narrow Intelligence involves applying AI only to specific tasks.

The existing AI-based systems that claim to use artificial intelligence are actually operating as a weak AI. Alexa is a good example of narrow intelligence. It operates within a limited predefined range of functions. Alexa has no genuine intelligence or self-awareness.

Google search engine, Sophia, self-driving cars and even the famous AlphaGo, fall under the category of weak AI.

Commonly known as strong AI, Artificial General Intelligence involves machines that possess the ability to perform any intellectual task that a human being can.

You see, machines dont possess human-like abilities, they have a strong processing unit that can perform high-level computations but theyre not yet capable of thinking and reasoning like a human.

There are many experts who doubt that AGI will ever be possible, and there are also many who question whether it would be desirable.

Stephen Hawking, for example, warned:

Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldnt compete and would be superseded.

Artificial Super Intelligence is a term referring to the time when the capability of computers will surpass humans.

ASI is presently seen as a hypothetical situation as depicted in movies and science fiction books, where machines have taken over the world. However, tech masterminds like Elon Musk believe that ASI will take over the world by 2040!

What do you think about Artificial Super Intelligence? Let me know your thoughts in the comment section.

Before I go any further, let me clear a very common misconception. Ive been asked these question by every beginner:

What is the difference between AI and Machine Learning and Deep Learning?

Lets break it down:

People tend to think that Artificial Intelligence, Machine Learning, and Deep Learning are the same since they have common applications. For example, Siri is an application of AI, Machine learning and Deep learning.

So how are these technologies related?

To sum it up AI, Machine Learning and Deep Learning are interconnected fields. Machine Learning and Deep learning aids Artificial Intelligence by providing a set of algorithms and neural networks to solve data-driven problems.

However, Artificial Intelligence is not restricted to only Machine learning and Deep learning. It covers a vast domain of fields including, Natural Language Processing (NLP), object detection, computer vision, robotics, expert systems and so on.

Now lets get started with Machine Learning.

The term Machine Learning was first coined by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms of technological advancements.

In simple terms,

Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically by feeding it tons of data & allowing it to improve through experience. Thus, Machine Learning is a practice of getting Machines to solve problems by gaining the ability to think.

But how can a machine make decisions?

If you feed a machine a good amount of data, it will learn how to interpret, process and analyze this data by using Machine Learning Algorithms.

What Is Machine Learning Artificial Intelligence With Python Edureka

To sum it up, take a look at the above figure:

Now that we know what is Machine Learning, lets look at the different ways in which machines can learn.

A machine can learn to solve a problem by following any one of the following three approaches:

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Supervised learning is a technique in which we teach or train the machine using data which is well labeled.

To understand Supervised Learning lets consider an analogy. As kids we all needed guidance to solve math problems. Our teachers helped us understand what addition is and how it is done.

Similarly, you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing but the training data set.

Supervised Learning Artificial Intelligence With Python Edureka

Consider the above figure. Here were feeding the machine images of Tom and Jerry and the goal is for the machine to identify and classify the images into two groups (Tom images and Jerry images).

The training data set that is fed to the model is labeled, as in, were telling the machine, this is how Tom looks and this is Jerry. By doing so youre training the machine by using labeled data. In Supervised Learning, there is a well-defined training phase done with the help of labeled data.

Unsupervised learning involves training by using unlabeled data and allowing the model to act on that information without guidance.

Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that this image is Tom and this is Jerry, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.

Unsupervised Learning Artificial Intelligence With Python Edureka

For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2.

Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.

Reinforcement Learning is a part of Machine learning where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions.

Imagine that you were dropped off at an isolated island!

What would you do?

Panic? Yes, of course, initially we all would. But as time passes by, you will learn how to live on the island. You will explore the environment, understand the climate condition, the type of food that grows there, the dangers of the island, etc.

This is exactly how Reinforcement Learning works, it involves an Agent (you, stuck on the island) that is put in an unknown environment (island), where he must learn by observing and performing actions that result in rewards.

Reinforcement Learning is mainly used in advanced Machine Learning areas such as self-driving cars, AplhaGo, etc. So that sums up the types of Machine Learning.

Now, lets look at the type of problems that are solved by using Machine Learning.

There are three main categories of problems that can be solved using Machine Learning:

In this type of problem, the output is a continuous quantity. For example, if you want to predict the speed of a car given the distance, it is a Regression problem. Regression problems can be solved by using Supervised Learning algorithms like Linear Regression.

In this type, the output is a categorical value. Classifying emails into two classes, spam and non-spam is a classification problem that can be solved by using Supervised Learning classification algorithms such as Support Vector Machines, Naive Bayes, Logistic Regression, K Nearest Neighbor, etc.

This type of problem involves assigning the input into two or more clusters based on feature similarity. For example, clustering viewers into similar groups based on their interests, age, geography, etc can be done by using Unsupervised Learning algorithms like K-Means Clustering.

Heres a table that sums up the difference between Regression, Classification, and Clustering:

Regression vs Classification vs Clustering Artificial Intelligence With Python Edureka

Now lets look at how the Machine Learning process works.

The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement.

To understand the Machine Learning process lets assume that you have been given a problem that needs to be solved by using Machine Learning.

The problem is to predict the occurrence of rain in your local area by using Machine Learning.

The below steps are followed in a Machine Learning process:

Step 1: Define the objective of the Problem Statement

At this step, we must understand what exactly needs to be predicted. In our case, the objective is to predict the possibility of rain by studying weather conditions.

It is also essential to take mental notes on what kind of data can be used to solve this problem or the type of approach you must follow to get to the solution.

Step 2: Data Gathering

At this stage, you must be asking questions such as,

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