Introduction to Artificial Intelligence: It is the Future

Today, AI (Artificial Intelligence) is one of the most growing industries. It has numerous advantages and applications in many fields and so on. Netflix, Amazon Prime, Youtube, and many other top companies using AI features in their app.

It is used in different fields such as in

1. Autonomous Vehicles

2. Speech Recognition.

3. Healthcare industry.

4. Machine Vision

5. Gaming industry.

6. Creating Chatbots.

7. Agriculture, Finance, Marketing, and Banking sectors.

8. Natural Language Processing (NLP).

9. Computer Vision.

10. Robotics

And many more.

So What is AI?

The name itself is not new, but the technology is far more modern. AI can be defined as building smart machines capable of doing work much like a human or beyond it. So devices require somewhat human intelligence.

AI is mainly divided into three types based on its categories:

1. Weak /Narrow /Artificial Narrow Intelligence (ANI).

2. Strong/ Artificial General Intelligence (AGI).

3. Artificial Super Intelligence.

We are currently at Artificial Narrow Intelligence. If AI goes to AGI (Artificial Super Intelligence), it will change the whole human life.

AI is a core branch of Computer Science in which there is Machine learning (ML) and Deep learning (DL). Deep learning is a subset of Machine learning, while Machine learning is a subset of Artificial Intelligence.

In short, to be a master in AI, you have to be a master in Machine learning and Deep learning as well.

Now, What is Machine learning?

To create a machine capable of performing a task like a human, it should have much similar intelligence as a human. This can be done by Machine learning.

In simple words, we have to teach the machine, provide some data.

For example, to identify animals via a device or any system, we have to offer numerous images of animals, provide more numbers of data for animals. We have to provides data to a machine to be capable of thinking and working.

A more advanced machine required much more data to be filled. In the beginning, Machines have to be provided with data. Afterward, it can able to take data from the environment to learn further, inspect further.

Machine learning can be broadly classified into three types:

1. Supervised learning: In this type of classification, labeled data is provided to the machine. So that it gives a decision, for example, providing labeled data in the form of images of dogs to a device can quickly learn to identify dogs.

Here are some of the most critical supervised learning algorithms.

* k-Nearest Neighbors

* Linear Regression

* Logistic Regression

* Support Vector Machines

* Decision Trees and Random Forests

2. Unsupervised learning: In this type, Unlabelled data is provided in a group/cluster to the machine. The machine is capable of learning with this Unlabelled data.

For example, in Unsupervised learning, Workers in a company are group together according to their specifications based on some algorithms.

Some algorithms for Unsupervised learning.

* Clustering

1. k-Means

2. Hierarchical Cluster Analysis

3. Expectation Maximization

* Visualization and dimensionality reduction

1. Principal Component Analysis

2. Kernel PCA

3. t-distributed Stochastic Neighbor Embedding

* Association rule learning

1. Apriori

2. Eclat

3. Reinforcement learning: Reinforcement learning works in different ways. Here, the learning system is called an agent. The agent observes the environment and performs some tasks.

If tasks are done in the right manner/succeeded, the reward is given in return, or else penalties for the Job failure. For example, Robot uses Reinforcement learning to learn to walk.

Ok, We understood AI and ML.

So What is DL?

Deep learning is a subset of ML. It mainly deals with neural networks. It is a vast neural network concept.

Due to deep learning, machines are capable of reacting in different situations; for Example, Autonomous Vehicles. It should be deal with various conditions such as understanding signals, Other vehicle distance, When to stop When to start, Where to speed up, and many more. It can be done using a broad set of labeled data and neural networks.

How to learn?

Learning a specific topic might confuse you at the beginning. After that, you can deal with it.

Learn through

1. Youtube videos.

2. Books. ( Best books are available to learn)

3. Udemy, Coursera, or any such related platform (They provide a certificate as well).

You have to be consistent. Provide at least 2–3 months to be perfect in AI, ML, and DL. Just starts with Deep learning and go to Machine learning and then shift to Artificial Intelligence.

Here you have to learn various categories for neural networks after that algorithm in ML then start to build a project. Software is available widely to use such as Jupyter, Tensorflow, Keras, Scikit-learn, and many more.

Why do all this stuff?

Artificial intelligence is one of the fields that makes you a billionaire, by Job or by a Startup. The more advanced sector requires more knowledge and then leads to more money. The whole world is concentrating on AI, Researching in various fields.

Making autonomous vehicles is a good example. So if you want to go to the Technology sector, it is one of the top skills that you have to learn.

Learn more about Machine learning through the below post.