Perhaps the earliest forms of machine learning experiments date back to the 1950s. The famous science fiction author, Robert Heinlein, allows readers to delve into a machine learning experiment of a seasoned mechanical engineer in the book “A Door into The Summer.” The protagonist successfully creates an intelligent machine that consists of Thorson Tubes and complex side circuits.
Challenges experienced during the experimentation of Machine Learning
A Door into The Summer proved to be more than a good read for science fiction enthusiasts. In fact, more scientists began to seriously consider the possibility of creating automated dishwashers and driverless motor vehicles. The drive to enhance machine learning during the 60s led to the development of Bayesian Methods.
Why are Bayesian Methods so important? They enable email service providers to filter out spam messages. Software engineers create image-processing software, turbo codes, and file classification systems using Bayesian Principles.
Despite the wide application of Bayesian Methods, software engineers in the 60’s still had a long way to go before they realized their machine learning dreams. Below are the major challenges that stood in their path to success.
1. Basic computer programing languages
It’s no secret that most programming languages that existed before the 60’s possessed mostly basic features. They only allowed software engineers to create applications that relied on internal databases to execute a limited number of features. As a result, most academic institutions prepared software engineers to solve the current market needs using somewhat basic principles.
2. Insufficient information on machine learning
A lot of machine learning breakthroughs experienced in the 60s and 70s were results of trial and error. Artificial Intelligence pioneers such as Jerome Wiesner and Claude Shannon literally resided in their labs in an effort to produce highly intelligent computers and household appliances. But not every passionate software developer had access to well-equipped laboratories and lavish research budgets. Even some renowned universities had done little research on machine learning.
These machine learning skills will turn you into a pro software engineer
1. Give the data exposure
You can use a number of software engineering techniques to validate an application and confirm whether it runs on correct standards. In fact, you can use a number of mathematical step-by-step methods to show the interdependence of co-related variables and importance of constant values.
The best way of validating a system is by testing it using real-world data. A software engineer should also test the system in a practical day-to-day situation, since experiments involve controlled environments and variables.
If you’ve just created a banking application that spots fraudulent banking practices, the best place to test the system is inside an actual bank.
2. Develop a sharp eye for superior models
Predictive machine learning software relies on complex probability models in order to produce results. Multinational companies rely on machine learning software to predict the future prices of stock, and increases in production costs. Apart from producing accurate results, a good machine-learning model should also be quick to adapt to changes that take place in the real world.
When faced with choosing between two machine learning models, the rule of thumb is to select the model that's closely related to real-life settings. A highly accurate model will serve you for a long time without the need of re-programming the system in order to adapt to changes happening in the market.
3. Verify your conclusions more than once
If you’re almost confident your machine learning software possess the highest level of accuracy, you should try testing it in different situations. Statistical flukes sometimes occur when the model tends to manipulate data into a forced direction. The same flukes can occur if you use outdated information and principles to obtain results.
A good machine learning system is one that continuously adapts to changing principles. This requires your input and frequent monitoring. It’s also advisable to do comparisons with other software applications in your specific industry.
4. Develop a variety of machine learning models
Did you know that there at least 10,000 versions of Facebook running all over the globe at the same time? The logic behind this approach is to enable Facebook’s software engineers to capture real-time challenges experienced on different continents, using sophisticated machine-learning algorithms. The large diversity among Facebook’s members makes it difficult to satisfy everyone’s needs using just one version.
Seasoned software developers agree that one gains better insight into a problem when creating an application to provide the required solutions. It makes sense to create different versions of a machine learning application, rather than creating entirely new systems from scratch.
Do you need reliable assistance with a tough end-of-semester or final year machine learning project? Get in touch with a pool of seasoned software developers at freelancer.com.
5. Make sure your model is relevant to the situation at hand
If you shoot without aiming, you miss the target. The same principle applies to the development of machine learning applications. Taking adequate time to do thorough research will enable you to fully understand the problem, rather than working on faulty assumptions. Conducting research will also help you develop a machine learning model that addresses other issues directly related to the main challenge.
One reliable way of ensuring your machine learning model is relevant to the issue at hand is recasting the problem using various approaches. Once you have the correct understanding of the situation, you’ll spend less time coming up with mathematical equations and commands for your application.
6. Target small areas then work your way up
If you’ve just finished developing your machine learning application, consider testing it in a controlled environment. Use a few real-life variables to see how your model relates to the end user of the system. This type of experiment will enable you to forecast challenges that are likely to arise if the model runs on a larger and more complex scenario.
7. Focus on growing your skills
Machine learning applications aren’t just for large companies that spend millions of dollars on advertising. Nowadays, even small and medium-sized businesses use machine learning on their websites to help customers identify products or services that match their keywords. This is why each system admin should aim to enroll in at least one machine learning course every year.
8. Pay attention to sharpening your domain knowledge
Domain knowledge refers to the amount of information a software engineer has in regards to the machine learning application's environment, and the relationship between internal and external components. Improving domain knowledge enables a software engineer to quickly identify suitable approaches for various challenges.
9. Always develop your coding skills
In order for your machine learning model to work correctly, you need to learn how to make the application execute commands based on the training data provided. Coding will enable you to create multiple versions of your application. When you have a deep understanding of coding machine learning applications, you’ll have less stress when it comes to editing the model to match changes in the industry.
10. Learn statistics
Statistics is a complex subject. It contains a lot of tasking formulae, numerous rules when dealing with variables, and complex applications. However, computer programmers need to understand these principles in order to create highly accurate predictive software. You need to understand the rules of probability to avoid making faulty models.
11. Improve your data modeling skills
Data modeling refers to the exercise of identifying valid correlations and existing patterns present in a set of collected data. This process enables software engineers to determine the correct requirements and framework when designing a machine learning model.
Machine learning is an exciting and dynamic field of software development. Paying attention to the 11 tips explained in this article will not only make you better, but will improve your chances of landing that dream job.
Now that you know how to sharpen your machine learning skills, feel free to share this article on Facebook or Twitter with your fellow students, or colleagues in the office.