Machine Learning

Hey Readers, 

                     Welcome to this Anonymous Web. We will learn about the modern technology as MACHINE LEARNING.

So, let's get started to know about MACHINE LEARNING.

 MACHINE LEARNING

Machine learning ( ML ) technology is growing day by day. Most of robotics and machine company use this ML technology. Machine learning uses different algorithms for advanced technology and give important details about various technical models, predictions of technology. In this time, It is mostly used for recognition, such as, Image recognition, Speech recognition, Facebook viewers, company's system and many more. 

So, Now we will see some amazing facts and information about MACHINE LEARNING

The machine learning is that technology, where the Artificial Intelligence and human brain meet.

WHAT IS MACHINE LEARNING (ML)

ML is that technology, where human can give a task to machine. Machine can follow the command of human and give that type of response. Machine can also learn different things from the past experience of Data, and compare itself from human kinds. Machine learning brings computer science and statistics together for creating predictive models.

The Machine learning term is first introduced by ARTHUR SAMUEL in 1959. At that time, it is generally used for common work and data,Like, Decision, Ordinary models. But, rising in the technology, ML can do complex work and solve any hard data with in time.

To learn Machine learning, First install PYTHON programming language  with compatible IDEs ( Integrated Development Environment )

HOW THE MACHINE LEARNING WORK?

The ML can learn from the past data and give some basic information about prediction models. After that step, ML start to manage the algorithm of Data and build logical data for models. It is the important part of Machine learning, because without logical data and algorithms, the ML is not working. Lastly, It include new data and end the given task. It include a new data, not only but also it add precious data in old one and make a amazing data structure.

FACTOR OF MACHINE LEARNING 

1. It is a data-driven technology

2. It can include new data in old one and improve the quality of ML.

3. Machine learning is as same as Data Mining, because it also control a massive amount of data.

4. Machine learning use data to detect different kind of Data Base.

CLASSIFICATION OF MACHINE LEARNING

The machine learning can be divided into three parts :-

1. SUPERVISED LEARNING

2. UNSUPERVISED LEARNING

3. REINFORCEMENT LEARNING

Here, some information about this classification of ML

1. SUPERVISED LEARNING 

                            

In this learning machine learn in presence of Supervision.

                               In this type of learning people give some task to the machine learning system. This system create a common model to understand the entire code. Then, it give accurate response to people with in time. The supervised learning is based on supervision, and it is the same as when a student learn various things in attendance of teacher. The clear example of this technology is SPAM FILTERING.

It is divided into 2 groups :-

1. CLASSIFICATION

2. REGRESSION 

2. UNSUPERVISED LEARNING

                             In UNSUPERVISED LEARNING machine can learn without any supervision. The data is not classified or organised in this kind of system. The algorithm act on data without any supervision. It reconstruct the whole data base with new features, group of objects. It tried to find important data from device, it can be further classified into 2 part :-

1. CLUSTERING

2. ASSOCIATION      

3. REINFORCEMENT LEARNING 

 

                             Reinforcement learning is totally based on feedback method. In this learning, the person who gave task to machine, he get rewarded for taking action by machine. They can learn many different things to improve knowledge and development. Explore the nature and environment and react that further type of reaction. The robotics machines, who interact with human kinds and negotiate with them.

 APPLICATION OF MACHINE LEARNING

Machine learning technology is useful in human's lifestyle in everyday. Step by step, we can use ML in daily life. Such as, Image recognition, Stock marketing, Online fraud detection and many more, which is described in further details. 

1. SELF-DRIVING CARS

2. VIRTUAL PERSONAL ASSISTANT

3. SPAM ( EMAIL ) & MALWARE  

4. TRAFFIC PREDICTION

5. PRODUCT RECOMMENDATION

6. IMAGE RECOGNITION

7. SPEECH RECOGNITION

8. AUTOMATIC LANGUAGE TRANSLATION

9. MEDICAL DIAGNOSIS

10. STOCK MARKET TRENDING

11. ONLINE FRAUD DETECTION

MACHINE LEARNING CYCLE

                                    Machine Learning system have ability to learn without programmed, But how the system is working? So, for describing this cycle, you may find the answer of this question. The main purpose of this cycle is to solve the problem or project.

1. DATA GATHERING

2. DATA PREPARATION

3. DATA WRANGLING

4. DATA ANALYSIS

5. MODEL TRAINING

6. MODEL TESTING

7. DEPLOYMENT

From this following cycle aspects, Machine can learn the entire data and create a interesting solution of the problem. It create a model of the solution through training, But for training a model, first to collect a data. So, the cycle is start by collecting a data.

1. COLLECTING DATA 

                                   This is the foremost step of cycle of machine. It obtain all the important data from client and try to resolve it. Data can be collected through various data source such as, files, database, internet, mobile devices. It is the most important step of the cycle. If the more data will be collected, the result will be more accurate. 

The following task are included in this step

1. Collect Data

2. Integrate the data from different sources

3. Identify various source

2. DATA PREPARATION

 

                                   After collection, the preparation of data is important in cycle. In this step, put the data on further place and start preparation of data. It gather data and randomize the data of order.

This is divided in 2 modules

1. Data Exploration

2. Data pre-processing

3. DATA WRANGLING

                                   When the preparation is completed, the wrangling process start in Machine learning cycle. The format of this wrangling is converting and cleaning raw data. It is cleaning the data and use better format to manage the data and transforming in a proper format for analysis. This is the most important thing to complete the process. 

There is some issue with wrangling, because some times the data may have

1. Duplicate Data

2. Noise

3. Missing Values

4. Invalid Data

4. DATA ANALYSIS

                                   After cleaning and preparing, the process done with Analysis. In Analysis some step play vital role to improve the cycle of Machine learning

1. Analytic technique of selection

2. Building models

3. Result

Analyze data is essential factor in cycle, because it build the data structure. It determine the types of problem and try to evaluate that data. Hence, in this step, building the model is main part of the cycle.

5. MODEL TRAINING 

                                 In this step, training the model for it better performance and outcomes of the problems. Database is used for the model training of Machine learning. For it understand in some patterns, rules, features, the training is required.

6.  MODEL TESTING

                                Once, the training of machine is completed, the testing take place in further step. In this testing, the accuracy of model is being tested by database. It determine the percentage of the accuracy of model testing. 

7. DEPLOYMENT

                               This is the last step of Cycle of Machine learning, where the model is completely ready to survive in the world. If all the above steps is perfect of model and give accurate result, then the data base give access to deploy the model in the real world. But, if in any step the model give any wrong outcomes, it would be rejected by Database.

There are some differences between Artificial Intelligence and Machine Learning

In this present time, Both these technology AI and ML is trending technology in all over this world, which is used to create intelligent system. Many people relate these technologies as same, but these are different from each other. 

To know about more on ARTIFICIAL INTELLIGENCE click below!!

                 💻   ARTIFICIAL INTELLIGENCE  💻

                                     

and the above information is on MACHINE LEARNING.

Artificial Intelligence                                  

1. The AI system make a smart and super computer, which give instant outcomes      to the people.

2. It has a very wide range of group.

3. The subset of AI is Machine learning and Deep learning.

4. It is easily interact with humanbeings, without any issue.

5. AI is divided into 3 parts, which is 1. WEEK AI, 2. GENERAL AI, 3. STRONG      AI

6. The applications of AI is Siri, catboats, online game playing, Intelligent human      robots.

7. It include reasoning, learning and self - correction.

Machine Learning

1. Machines create an accurate result for further problem, So, it is used to                problem solving machine.

2. It has a limited range of group.

3. Deep learning is the main subset of Machine Learning.

4. It is just give a task response to the client. So, it is connected with people but      for only solve issues. Not easily interact with people.

5. ML is also divided into 3 parts, 1. Supervised Learning, 2. Unsupervised          Learning 3. Reinforcement learning

6. The main application of this ML is recognition, Algorithms, Personal Assistant.

7. It include a new data with introduction and also it include learning and self-          correction.

Thus, this is all about MACHINE LEARNING. I hope you may find important and amazing information of This system.

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