Introduction to Machine Learning

I believe to express my experience and knowledge without any 

boundary and specially those who has less understanding about 

the Artificial Intelligence, Machine Learning and Deep Learning 

that's why I don’t prefer to write advance vocabulary. After Reading

 this Article you can able to understand what is Machine learning

 and its types and commonly use algorithm on it. If you get to know 

about the basic definition and understating of Machine learning read

 my previous Article Differences amongArtificial Intelligence, 

Machine Learning and Deep Learning.

[Article writer]

Machine Learning Introduction: 

           There is a variety of Learning, Learning Machine Learning - If we talk to AI, initially with its algorithm to solve any problem in the computer I used to give it - It is called Symbolic AI- in modern AI, we only give examples to computer- computer itself learns from these examples or data. for example, if we have to make a difference in the cat and dog, The pictures will show to the computer and show similar photos of the dog - computer itself will learn to make the difference between cats and dogs these methods are called machine learning. This is the modern algorithm of the AI's current work on this principle.

In machine learning, we divide our data into three parts.

  Training Data:

        In machine learning first, we teach computers through examples, this is called
training - we tell both computers and the answer to the computer for example,
we give the computer a picture of a dog, and it also tells us that this dog The
image is - If the computer answers the wrong (for example, he tells him the cat)
then re-adjusts himself so that the next time he does not make mistakes, the
process is called learning - for which the algorithm was the most used Is it
called Gradient Descent-like this, our model gets train. 

Gradient descent is a first-order iterative optimization algorithm
for finding the minimum of a function. To find a local minimum of a function
using gradient descent, one takes steps proportional to the negative of
the gradient (or approximate gradient) of the function at the current point.

 [Wikipedia]

      Validation Data:

         
         Once we have a test, then we test the capability of our model. This is the validation data used for this purpose. If our model is not doing the right thing, then we change our model hyper parameters let’s do this, this process continues even if the model's accuracy is not very good.
    Testing Data:
    When our training is completed, we last check our model on test data- The purpose of isolating this data is that the validation data is exposed during Hyper-parameter's collection (or training) - any of us however, the data needs to be checked on the data.

Neural Network: 

          The Neural Network is designed to be influenced by the human mind - it has neuron's basic condition - a neuron takes some inputs and multiply it with weights and makes linear transformation application- as seen in below diagram. 

The Neural network consists of such neurons, a neuron is connected to its next neurons and gives it data. A neuron also appeals an Activation function before giving data to its next neurons. The Neural Network can model any kind of non-linear data.


A Neural network usually has an Input Layer, Hidden Layers, and an Output Layer. (Which is deeply disease in the Difference among Artificial Intelligence, Machine Learning and Deep Learning Article)  In Deep Learning, we increase the number of layers that represents the model Complex data another important thing is that features do not exclude in Deep Learning - Model removes itself through the transformations of different layers.

Types of Machine Learning:

Now, let’s understand the types of machine leaning with basic view. 

Supervised Learning:

        In this way, we provide data and it’s labeling during training, adjusting itself when looking at computer labels - as a computer has an answer. In simple word we can say that, it is consist on dependent variable which predict from the predictor set of data we can also say it independent variable. By means of variable’s set, we get out desired output, generating a function that map inputs. Example of Supervised Learning is Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

Unsupervised Learning:

        In this method we do not provide feedback or target the actual purpose is to transfer data or better understanding the data. In clustering that is a kind of un-supervised learning, we divide the data into different groups. Data is the same as it is in a group Dimensionally reduction is another type in which we reduce the size of the data.  It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.

Reinforcement Learning:

       In this method learns from computer feeds of the environment - for instance, if a vehicle is operating automatically, it can adjust itself to the other vehicles and road conditioning on the road similarly used in games etc.

Example of Reinforcement learning Markov Decision Process.

Machine Learning Algorithm:

Commonly used Machine learning Algorithm which can be applied any kind of data problem (almost). 

• SVM
• K-Means
• Random Forest
• KNN
• Logistic Regression
• Decision Tree
• Naive Bayes
• Linear Regression
• Dimensionality Reduction Algorithms
• Gradient Boosting algorithms

                    GBM
                   XGBoost
                   LightGBM
                   CatBoost