Introduction to Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

In simple words, Machine Learning is a sub-part of Artificial Intelligence that learn from example and experience, without being the explicit programme. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given.





              A computer program is said to learn from experience E with   some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” -Tom M. Mitchell

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field.


Example of Machine Learning 

There are many examples of machine learning .here are some of them
   
1.  E-mail   filtering
2.  Face Detection
3.  handwriting Detection
4.  Weather Detection
5.  Medical  Diagnosis
6.  Image Classification
7.  Stock prediction
8.  Agriculture fields
9.  Banking Services

there are many fields where machine learning is used.

 The need of Machine Learning?

As we know that machine learning is raised from Artificial intelligence. 
In the single line, "  we can say that human face many problems in a complex task whereas machine did the same task better than human  ", also machine task less time than a human.

Machine learning is needed when a task is too complex for a human to code directly. we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.


Machine learning is useful for finding relationships between things, especially in very large data sets which are too big for humans to efficiently process. It can be used in object recognition, marketing analytics, analyzing data in labs, and numerous other applications involving large amounts of data that need to be analyzed.

Kind of Machine Learning 

There are four kinds of machine learning algorithm 

1)  Supervised learning

2) Unsupervised  learning 

3) Semi-supervised learning

4) Reinforcement learning


1) Supervised learning: Supervised machine learning algorithm can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. In a simple supervised learning, an algorithm is used when given data is labeled.


Supervised learning problems can be further divided into two parts, namely classification, and regression.

Classification: A classification problem is when the output variable is a category or a group, such as “black” or “white” or “spam” and “no spam”.

Regression: A regression problem is when the output variable is a real value, such as “Rupees” or “height.”


2) unSupervised learning: unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.

Unsupervised learning problems can be further divided into association and clustering problems.
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”.
Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

3) semi-supervised learning: Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.


4) Reinforcement learning: Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.


  •    these all are some basic things about machine learning.
  • before start machine learning you must need basic of maths not required too much.
  • to understand the machine-learning algorithms connect them to real life.

if you have any query and problem .please ask me in a comment I should try to give my best.