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Hi,
As per your query it seems that you have completed your class 12th in 2007 and now wish to pursue undergraduate degree from universities abroad.
So as per your profile it would be difficult for you to get admission in abroad universities.
The first thing the university would ask you is that what were were you doing for all these years, however if you have a valid reason for this gap then your application might be considered by some universities.
So as per your query I would suggest you to:
1. Select the country where you wish to study.
2. Once the country is decided then you need to decide which course you wish to pursue.
3. After deciding the course, then you need to shortlist the universities offering course of your interest.
4. Once the universities are shortlisted you may then contact the admission department of these universities either through email or Phone to know whether are eligible for admission or not.
4. If you meet the eligibility criteria you may then apply accordingly.
For your reference find below some of the finest study destination to pursue undergraduate degree:
1. US.
2. UK.
3. Canada.
4. Australia.
5. New Zealand.
Feel free to raise more questions. Appreciate by giving it thumbs up or marking as best.
All the Best.

Radial basis function (RBF) network are the most computational intensive. Radial basis functions are powerful techniques for interpolation in multidimensional space. A RBF is a function which has built into a distance criterion with respect to a center. Radial basis functions have been applied in the area of neural networks where they may be used as a replacement for the sigmoidal hidden layer transfer characteristic in multi-layer perceptrons. RBF networks have two layers of processing: In the first, input is mapped onto each RBF in the 'hidden' layer. The RBF chosen is usually a Gaussian. In regression problems the output layer is then a linear combination of hidden layer values representing mean predicted output. The interpretation of this output layer value is the same as a regression model in statistics. In classification problems the output layer is typically a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, known as ridge regression in classical statistics and known to correspond to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework.
RBF networks have the advantage of not suffering from local minima in the same way as Multi-Layer Perceptrons. This is because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer. Linearity ensures that the error surface is quadratic and therefore has a single easily found minimum. In regression problems this can be found in one matrix operation. In classification problems the fixed non-linearity introduced by the sigmoid output function is most efficiently dealt with using iteratively re-weighted least squares.
RBF networks have the disadvantage of requiring good coverage of the input space by radial basis functions. RBF centres are determined with reference to the distribution of the input data, but without reference to the prediction task. As a result, representational resources may be wasted on areas of the input space that are irrelevant to the learning task. A common solution is to associate each data point with its own centre, although this can make the linear system to be solved in the final layer rather large, and requires shrinkage techniques to avoid overfitting.
Associating each input datum with an RBF leads naturally to kernel methods such as support vector machines

Hiiii Urvashi.First of i would like to tell you that i am reallly feeling overwhelmed to write you.Very few girls are passionate about career,future and goal,you are one of such rare girl among them.Youa re really taking great pains to establish yourself.Anyways do read the contents carefully.
As the saying goes teaching is a nobel profession, it is true in the real sense of words. Teaching has an influence in developing ones mind and character and also gives the great job satisfaction. As this field is so vast, it needs to have different skills and type of training for different levels. Each level calls for a different expertise. If you love to be with children, you can go for NPTT.
NPTT is a Nursery Primary Teacher Training which makes you eligible to teach in Play schools, Nursery schools, Primary/elementary schools or Self employment by starting institutes/tutorials. NPTT is generally done after 10+2 but there is no upper age limit to do this program.
Don't opt for FD,because it has many disadvantages.I am not demotivating you but telling you true facts,Students pursuing a degree in fashion design should be prepared to work hard and learn many skills including draping, sewing and pattern making. Although there are many benefits to a career in fashion design, no career is glamorous at all times.
Long Hours-A budding fashion designer must be prepared to work long hours. Fashion houses may be working under deadlines or working with clients to meet their specific needs. During times like this, which could be ongoing, fashion designers could work 12-hour days. Because the industry is competitive, an individual must be willing to work these hours or be easily replaced by someone else.
Daily Chores-Many people may picture an average day in the life of a fashion designer as sketching ideas and working with luxurious fabrics all day. However, this is not reality. Fashion designers have various chores on a daily basis. Completing paperwork, such as specification sheets, may be a daily function. Specification sheets include measurements, color sample specifications and a description of the product.
Low Starting Salaries
A starting salary for a fashion designer may not be as glamorous as its job description. Low salaries are often the norm for an entry-level design position, notes Fashion.
till here is it clear Urvashi,now read next

Hi,
MBA in finance is a good course to pursue and it offers good career prospects and excellent growth opportunities.
As per your profile you may look out for exciting careers in banks, financial consultancies and financial institutions.
You may get involved in consumer and investment banking, institutional finance, merchant banking, corporate finance and international finance.
A good experience in this field can also lead you to serve on government committees and contribute significantly to policy-making issues.
You may also end up providing consultancy services to corporate and government organizations.
The remuneration of MBA finance graduates is dependent upon the type of organizations that takes them in.
Those who work as financial analysts start off with a minimum package of Rs 4 lakh an year.
Credit analysts start at Rs 5-6 lakh per annum and earn as much as Rs 20-30 lakh per annum once they reach senior level positions.
Management consultants too start at Rs 5-8 lakh and within a few years earn as much as Rs 10-15 lakh per annum.
I would request aru.chopra to share her expert opinion on this.
Feel free to raise more questions. Appreciate by giving it thumbs up or marking as best.
All the Best.