Today , AI (Artificial Intelligence) is one of the most growing industry. It has numerous advantages in many fields and so on. Advan...
Today , AI (Artificial Intelligence) is one of the most growing industry. It has numerous advantages in many fields and so on.
Advantages 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 new. AI can be defined as building smart machines capable of doing work much like human or beyond it. So machines require somewhat human intelligence.
AI is mainly divided into 3 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. lf AI goes to AGI (Artificial Super Intelligence) it will change 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 master in Machine learning and Deep learning as well.
Now What is Machine learning ?
To create a machine capable to perform task like human, its should have much similar intelligence like human. This can be done by Machine learning.
In simple words, we have to teach machine, provide some data. For example to identify animals via machine or any system , we have to provide numerous images of animals, provide more numbers of data for animals. We have to provides data to machine to be capable to think and work. A more advanced machine required much more data to be filled it. In beginning, Machines have to be provided with data. Afterward it can able to take data from environment to learn further , inspect further.
Machine learning can be broadly classified into 3 types:
1. Supervised learning : In this type of learning , labelled data is provided to machine.So that it gives decision. For example providing labelled data in the form of images of dogs to a machine can easily learn to identify dogs.
Here are some of the most important 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 machine. The machine capable to learn with this Unlabelled data.
For example in Unsupervised learning, Workers in a company are group together according to their specification 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 way. Here, the learning system is called an agent . The agent observe the environment and perform some task. If task are done in right manner/succeded reward are given in return or else penalties for task failed.
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. Basically it is a large neural network concept.
Due to Deep learning, machines are capable to react in different situations. For Example Autonomous Vehicles. It should be deal with various situations 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 large set of labelled data and neural networks.
Learn through
1. Youtube videos.
2. Books. ( Best books are available to learn)
3. Udemy, Coursera or any such related platform. (They provide certificate as well)
You have to be consistent. Provide at least 2-3 months to be perfect in AI , ML and DL. Be start with Deep learning and go to Machine learning and then to Artificial Intelligence. Here you have to learn various categories for neural networks after that algorithms in ML then start to build a project.
Software are available widely to use such as Jupyter, Tensorflow , Keras , Scikit-learn and many more.
Netflix, Amazon Prime , Youtube and many other top companies using AI features in their app. So if you really want to go in Technology sector. It is one of the top skill that you have to learn.
Advantages 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 new. AI can be defined as building smart machines capable of doing work much like human or beyond it. So machines require somewhat human intelligence.
AI is mainly divided into 3 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. lf AI goes to AGI (Artificial Super Intelligence) it will change 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 master in Machine learning and Deep learning as well.
Now What is Machine learning ?
To create a machine capable to perform task like human, its should have much similar intelligence like human. This can be done by Machine learning.
In simple words, we have to teach machine, provide some data. For example to identify animals via machine or any system , we have to provide numerous images of animals, provide more numbers of data for animals. We have to provides data to machine to be capable to think and work. A more advanced machine required much more data to be filled it. In beginning, Machines have to be provided with data. Afterward it can able to take data from environment to learn further , inspect further.
Machine learning can be broadly classified into 3 types:
1. Supervised learning : In this type of learning , labelled data is provided to machine.So that it gives decision. For example providing labelled data in the form of images of dogs to a machine can easily learn to identify dogs.
Here are some of the most important 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 machine. The machine capable to learn with this Unlabelled data.
For example in Unsupervised learning, Workers in a company are group together according to their specification 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 way. Here, the learning system is called an agent . The agent observe the environment and perform some task. If task are done in right manner/succeded reward are given in return or else penalties for task failed.
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. Basically it is a large neural network concept.
Due to Deep learning, machines are capable to react in different situations. For Example Autonomous Vehicles. It should be deal with various situations 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 large set of labelled data and neural networks.
How to learn ?
After learning specific topic, it might confused you at beginning. After that you have the ability to 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 certificate as well)
You have to be consistent. Provide at least 2-3 months to be perfect in AI , ML and DL. Be start with Deep learning and go to Machine learning and then to Artificial Intelligence. Here you have to learn various categories for neural networks after that algorithms in ML then start to build a project.
Software are available widely to use such as Jupyter, Tensorflow , Keras , Scikit-learn and many more.
Why to do all this stuff?
Artificial intelligence is one of the field that makes you billionaire, by Job or startup . More advanced machine require more knowledge. Whole world is concentrating on AI. Researching in various field. Making autonomous vehicles is a good example.Netflix, Amazon Prime , Youtube and many other top companies using AI features in their app. So if you really want to go in Technology sector. It is one of the top skill that you have to learn.
COMMENTS