Machine Learning Basic Concept

Artificial Intelligence (AI):

Artificial intelligence refers to the simulation of human intelligence in machine that are programmed to think like humans and mimic their actions. the term may also be applied to any machine that exhibits traits associated with human mind such as learning and problem-solving.

Artificial intelligence is based on the principle that human intelligence can be defined in away that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goal of AI include learning, reasoning and perception 

     Is just a quality of a machine that helps machine to do 5 discussed task.

              1.Machine Learning

              2.Statistics

              3.Optimization

Qualities of Artificial Intelligence 

     1.Estimate Prediction

     2.Classification

     3.Grouping | clustering

     4.Recommendation

     5.Decision making

     6.Generating Knowledge (Dream, paintings)

Machine Learning:

“Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience"

The scientific field of is a branch of artificial intelligence, as defined by computer scientist and machine learning

How does Machine Learning Work..?

Types of Machine Learning:

Supervised Learning:

The majority of practical machine learning uses supervised learning.

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance

• Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
• Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Some popular examples of supervised machine learning algorithms are:

• Linear regression for regression problems.
• Random forest for classification and regression problems.
• Support vector machines for classification problems

Unsupervised Learning:

      Unsupervised learning is where you only have input data (X) and no corresponding output variables.

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
• Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
• k-means for clustering problems.
• Apriori algorithm for association rule learning problems.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data. Unsupervised learning problems can be further grouped into clustering and association problems.

Reinforcement Learning :

      Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

     Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task.

   In the absence of a training dataset, it is bound to learn from its experience.

Deep Learning:

deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.