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Artificial Intelligence (abbreviated as AI) also known by the name of machine intelligence is an intelligence runs by machines in contrast with the neural intelligence presented by humans and animals. It basically explains machines that mimic the cognitive functions that a human combined with other human minds, activities like ‘problem solving ‘and ‘learning’.
For example, we can take OCR which is quickly added from things considered to be a part of AI, have become a routine technology. Modern machine ability that are generally classified as AI are military simulations, operating cars (features like voice recognition and driverless cars), intelligent routing in content delivery networks, understanding human speech successfully. AI can be categorized into 3 types:

1. Analytical:-It is called as cognitive intelligence that is learning from previous experiences and safeguarding the future from those problems..
2. Human-inspired:-cognitive as well as emotional intelligence both are in it which understands emotion of human and inspired them for decision making.
3. Humanized:-Social, cognitive an emotional intelligence is involved in it and is able to be self- self -aware and conscious in communication with others.

A Basic Artificial Intelligence analyzes the environment and proceeds for action for large amount of chance of success.

What is Machine Learning?

Machine learning (ML), is a basic approach of Artificial Intelligence research for the field’s inception, Computer algorithms is study in it that automatically improve themselves through experience just like humans do through self-learning. Most of its learning algorithms are built on mathematical models which are based on sample data known as training data, which governs it to perform the task without any explicit programming. Some of the uses are email filtering and computer vision as it is not feasible to develop algorithms for specific tasks.
In order to get the best performance with regard to common, the complexity of the given function should match that of the hypothesis. Machine learning and statistics are closely related fields. The unsupervised way of learning incorporates algorithms that builds a mathematical model from a set of data which only contains.
Python programming language is used mostly for Machine learning and Artificial Intelligence. Python fist more for data science.
Packages used in Machine learning programs are likepands, numpy, scipy, scikit-learn etc.

Jupyter Notebook is generally used for Python language programming for Machine Learning. It gives an interactive computational environment for writing codes and executing it of python codes based on applications of Data Science.

To lean more about python programming Click here

How Machine Learning Works?

A ML trains from past data, make the prediction models and when new data receives, it will give the result for it. The output accuracy total based on the amount of data trained, large amount of data builds better model due to which it gives more accurate output.

Steps which shows working of Machine Learning

1. Input past data →Training
2. Machine Learning Algorithms→ Learn from data
3. Building Logical Models
4. Output→ New data produces

Features of Machine Learning

1. It will be trained from previous data and improve automatically
2. It uses data to detects different patterns in produce dataset
3. Machine Leaning is data-driven technology.
4. It is somewhat common to data mining as both concern with large amount of data.

Some applications of machine learning are:a

1. Internet fraud detection
2. Adaptive websites
3. Financial market analysis
4. Machine learning control
5. Medical diagnosis
6. Robot locomotion
7. Search engines
8. Speech recognition

Classification of Machine learning

There are three classification of Machine learning at Broad level which as mentioned below:

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

1. Supervised learning:-Supervised learning algorithms are design in such a way that it learns by example. Large amount t of practical machine learning uses supervised learning. In supervised learning there are two variables required, one is input variables (x) and output variables(y) and in this situation we use algorithms to lean the mapping function providing input variables and output variables.
   Y=f(x)

   Supervised learning has further classified into two groups:

   1. Regression:-In this classification of supervised learning output variables is real value like weight, dollars etc.
   2. Classification:-In this group the problems is when output variable is based on category like green, red, water, diseases etc.
2. Unsupervised learning :-In the Unsupervised learning algorithms of Machine Learning there is only input variables(x) and no output variables corresponding to it. This is terms as unsupervised learning algorithms because unlike supervised learning no proper answer and no teacher for train the data.
   

   Unsupervised learning is further classified into two groups

   1. Association:-It is rules leaning problems where we can find rules that explains huge portions of your data such as people who want to purchase X also tend to purchase Y.
   2. Clustering:-Clustering problem is where you have to find inherent group in data like group customers by buying behavior..
3. Reinforcement learning:-In this classification of Machine learning, it is feedback-based learning algorithms methods in which learning data gets extra points for each correct action and get penalty for each incorrect action. The amin focus of this classification to achieves more points so that it improves their performance.
   Example of Reinforcement learning is “The movement of arms is automatically learning by robotic dog.

Challenges of Machine Learning

Now a days Machine Learning becomes one of significant strides in autonomous cars and cybersecurity due to Artificial Intelligence. There are many challenges from which Machine learning has not come out still are as discussed below:

1. Time Consuming:-It takes more time to train the dataset. Due to hug amount of datasets it time taken is more by Machine Learning models for data acquisition, features extraction and retrieval.
2. Quality of data:-It is necessary to collect good quality of dataset for training purpose which has becomes of challenges for machine learning.
3. Difficulty in Deployment:-It is very much difficult to deployed the machine learning model in real life.
4. Lack of Specialist:-There is no specialist developer for Machine Learning technology yet available.
5. Problems of overfitting:-If the Machine Learning model is overfitting, it will not be shown well for issue.

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