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Artificial Intelligence vs Machine learning vs Deep learning

Artificial Intelligence and Machine learning and Deep learning these are three different terms. and I am sure that you should be a confusion in your mind between them.

So today I am going to explain to you what are these three terms.

Artificial Intelligence vs Machine learning vs Deep learning

 First of all guys please read this post at the end of the word because with reading totally post you wouldn't understand what I am saying.

Artificial Intelligence is a big term under which the Machine learning and Deep learning comes.

Machine learning is a subset of Artificial intelligence. and Deep learning is a subset of Machine learning. So we can say that all three parts are the subset of each other.

So take the deep look to understand these three terms...

Artificial Intelligence:

The term AI(artificial intelligence) was the first existence in 1956. and within days it's become a popular subject in research.

Artificial Intelligence is a technique in which machines act like as a human. and the machines have their own brain and yourself take the decisions. 
Artificial Intelligence used to perform human tasks by a machine and AI can be made by processing a large amount of data with patterns.

but at the beginning of time, this idea was totally failed. because the human doesn't know how to prepare a machine code for an AI machine so because of this reason the machine learning comes into existence.

Machine Learning:

Machine learning comes into existence in the early nineties. and the main aim of Machine learning is how to train a robust version of the Artificial Intelligence system which is able to perform tasks like a human. and in machine learning, the problem is that we need to be collect the big amount of data for a machine.

because the machine only performs with codes and with the term of a large amount of data the Statistics and Neuroscience comes into existence and the statistics are used for making complex models with the huge amount of data and the Neuroscience is used to design models of brain for machines using neurons just like our brain.

So machine learning is a subset of Artificial Intelligence in which machines used the statical method and probability methods to adjust which task machine will be performed after a sudden task and this term enables the machine to improve the machine code with experience.

Example: A car that is automatically derived is running on the road. so when the road turns the car also turns automatically. this is possible because of Machine learning. but the fact is that machine learning only works on conditions. it has not the ability to think it's own.

But there is a fault that is machine consumes a large amount of time to complete a task.
So, because of this, Deep learning comes into existence.

Deep Learning:

Deep learning is also a kind of Machine learning and it's a part of Machine learning. in Deep learning, we only focus on the concept of Artificial Neural Network. we perform the functionality of Neurons to make a brain of Machine.

We can simply connect all the neurons in a systematic form and connect each other according to data patterns. and the fact of Deep learning is that the working of Neurons is not depending on a specified algorithm.

Another thing is that Neurons are just like a black box because no one knows what is going on Neurons.
Deep learning is a way to think on a big part of a particular object because the Machine learning only works on certain conditions.

For an example of how a machine should know wheater, an object is a dog or cat. here Machine learning fails. because the Machine learning is only checked certain conditions that if an object is small and has four legs and a tail. so that object is a dog but these conditions also work on a cat. so Machine is going to confuse. so here Deep learning is work. because Deep learning is used more valuable data.

Deep learning is used a large amount of data to make sure about an object. whereas Machine learning can easily work with small data.

Deep Learning Vs Machine Learning.

The execution time of a task in Deep learning is much much less than the Machine learning.
Deep learning depends and only work with higher perfection machines where Machine learning also works with lower perfection machines.
Machine learning breaks down a problem and solves the part of the problem and combined them together to produce the output. but Deep learning is used end to end method to solve a problem.

So the main theme is that Machine Learning is used algorithms to collect data and learn from data, and makes the decision based on his learning.

What you should learn in maths if you are going to learn maths...

Today the world and the technical community are going to research Artificial Intelligence and in the future, the AI must come.
and every technical lover and a technical man want to learn Artificial Intelligence. but for Artificial Intelligence the math is very important. because without math a robot can't work. 

So today I am going to tell you what you should learn first in maths for learning Artificial Intelligence and to make a career in AI.

what you should learn in maths to learning artificial intelligence?
Maths in Artificial Intelligence
There are two sections in maths which are essential to learn for AI(artificial intelligence) user.

  • Calculus
  • Linear Algebra.

These two sections are whole maths for Artificial Intelligence technology.


  1. Simple Derivative.
  2. Derivative using Geometry.
  3. Dot product using multivariable.
  4. Chain Rule.
  5. Exponential and Implicit functions.
  6. Limits.
  7. Integration.

Note:  these topics are written step by steps so I suggest you to a better understanding of these topics please learn these topics (a-g) by sequence. and don't try to escape any topics. just finished one topic and then go to the next topic. 

a) simple derivative gives you the knowledge of what is derivative and why it's used for. 

b) Derivative using Geometry gives you knowledge about where a particular function shoots to generate an answer. and to solve the complex functionality of the program. this also gives you the idea to pick a program among other programs which have minimum time complexity and minimum space complexity. the derivative also gives you the idea to measure the steepness of a function. this topic is also important for learning machine learning. 

c) Dot product gives you the knowledge to find the derivative of two or more functions. it's also used to finding the composition of the functions.

d) The chain rule is used to prepare the solutions for the neural network and used for making the neural network. so it's important for both artificial intelligence and deep learning.

e) these two terms (Exponential and Implicit) functions are used for making logic for solving a particular programming problem. and used to make the program more reliable and small.

f) Limits are used to predict the end of the program. and it's used for learning which should be the end of the program. 

g) The integration process is the inverse of the derivative. so we can integrate all the programs which are a particular neural network. and it's also used for making the brain of the machines and to set the neural networks. 

One thing is that when you completed all topics then you should try to use these terms on neural networks. so these term gives you the knowledge about how a neural network-related directly to a particular program.

Linear Algebra:

  1. Linear algebra.
  2. Vectors.
  3. Combinations of linear algebra.
  4. Transformation of linear algebra into a matrix.
  5. Matrix formulations.

a) Linear algebra gives you the basic knowledge of the algebraic function and I suggest you also find the answer to the question is that " why algebra" is most important in AI.

b) Vectors give you the knowledge about how to build a block of linear algebra to solve a particular problem and help you to building blocks of vectors. 

c) Combinations of linear algebra give you the knowledge about how to scale a vector into 2D and 3D. and how to visualize and add vectors into 2D and 3D. 

d) Transformation of linear algebra gives you the knowledge about how to transform a linear algebra into a matrix. this section is more effective when we are working with multiple algebraic functions. and this is the fine way to solve the algebraic functions. 

e) Matrix formulation gives you the knowledge about how to apply the maths on the matrix and by using which method to solve a particular matrix problem.

One another thing is that when you complete the second step then you should also try these terms on neural networks because this thing gives you the knowledge about that how the neural network directly depends on linear algebra. 

In this section, you most focused on these terms that 1. how to represent vectors into 2D and 3D in a graphical manner. 2. how to solve a simple equation of the vector. 3. how to determine the span of a particular vector. 4. how to solve the vector problems using the matrix.

So this is the last line of today is post..............

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