What are the reasons for using Machine Learning

Q.5) What are the reasons for using Machine Learning?

Ans: Following are some of the reasons: 

1. Some tasks cannot be defined well, except by examples. For example : recognizing people.
2. Relationships and within large amounts of data, To solve these shine learning and data mining may be able to find these relationships.
3. Human designers often produce machines that do not work as well as desire in the environments in which they are used.
4. The amount of knowledge available about certain tasks might be too large for explicit encoding by humans.
5. Environments change time to time.
6. New knowledge about tasks is constantly being discovered by humans.

Q.6) List the phases of machine learning?

Ans: Typically follows three phases :

1. Training: A training set of examples of correct behaviour is analysed and some representation of the newly learnt knowledge is stored. This is some form of rules.

2. Validation: The rules are checked and, if necessary, additional training is given. Sometimes additional test data are used, but instead, a human expert may validate the rules, or some other automatic knowledge - based component may be used. The role of the tester is often called the opponent.

3. Application: The rules are used in responding to some new situation.

Q.7) What is meant by machine learning ? What is its need to today's society ? Explain successful applications of machine learning ?

Ans.: Examples of successful applications of machine learning:

1. Learning to recognize spoken words.
2. Learning to drive an autonomous vehicle.
3. Learning to classify new astronomical structures
4. Learning to play world-class backgammon
5. Spoken language understanding: within the context of a limited domain,determine the meaning of something uttered by a speaker to the extent that it can be classified into one of a fixed set of categories

Face Recognition

• Face recognition task is effortlessly and every day we recognize our friends, relative and family members. We also recognition by looking at the photographs. In photographs, they are different pose, hair styles, background light, makeup and without makeup.
• We do it subconsciously and cannot explain  how we do it. Because we can't explain how we do it we can't write an algorithm. 
• Face has some structure. It is not a random collection of pixel. It is symmetric structure. It contains predefined components like nose, mouth, eye, ears. Every person face is a pattern composed of a particular combination of the features by analyzing sample face images of a person, a learning program captures the pattern specific to that that person and uses it to recognize if a new real face or new image belongs to this specific person or not.
• Machine learning algorithm creates an optinized model of the concept being learned based on data or past experience.

Q.8) Explain the difference between machine learning and data mining.

Ans.

Machine Learning: 

1. In machine learning the main goal is to learn a model, which can be used to predict future events.
2. It considered data as secondary.
3. Machine learning uses relatively complex and global models.
4. Only hundreds or thousands of examples in a training data set.
5. To learn one or few carefully defined models, this can be used to predict future events.

Data Mining: 

1. In data mining, the main goal is to discover new interesting information which describes the current  data set.
2. It considered data as primary.
3. Data mining uses simple models or local pattems.
4. Huge data sets, even millions of rows.
5. To find all interesting patterns which describe the data set

Q.9) What are the ingredients of machine learning ?

Ans.: The ingredients of machine learning are as follows :

1. Tasks: 

• The problems that can be solved with machine learning. A task is an abstract representation of a problem. The standard methodology in machine learning is to learn one task at a time. Large problems are broken into small, reasonably independent sub-problems that are learned separately and then recombined.
• Predictive tasks perform inference on the current data in order to make predictions. Descriptive tasks characterize the general properties of the data in the database '

2 Models: 

• The output of machine learning. Different models are geometric models, probabilistic models, logical models, grouping and grading.
• The model-based approach seeks to create a modified solution tailored to each new application. Instead of having to transform your problem to fit some standard algorithm, in model-based machine learning you design the algorithm precisely to fit your problem.
• Model is just made up of set of assumptions, expressed in a precise mathematical form. These assumptions include the number and types of variables in the problem domain, which variables affect each other, and what the effect of changing one variable is on another variable. 
• Machine learning models are classified as: Geometric model, Probabilistic model and Logical model.

3. Features: 

• The workhorses of machine learning. A good feature representation is central to achieving high performance in any machine learning task. 
• Feature extraction starts from an initial set of measure data and builds derived values intended to be informative, non redundant, facilitation the subsequent leaning and generalization steps 
• Feature selection is a process that chooses a subset of features from the original features so that the feature is optimally reduced according to a certain criterion.