Prachi is a Machine Learning Engineer, working in the respective field since 2015.
Commonly used in maths and computer science, an algorithm is a set of instructions, written to solve a particular problem or perform certain computational actions. The general actions include data processing, performing calculations, automated reasoning, and other similar tasks.
An algorithm is said to be an effective one if it’s written within an optimized amount of time and space as well as in a clearly understandable formal language.
In machine learning, algorithms play a key role in performing simple to complex actions.
Machine learning is a wide field with an innumerable number of algorithms available to solve a single problem. You need to be decisive in selecting the most appropriate algorithm for a particular problem or computation. Consequently, it is important for you to take control of which algorithms best suit your tasks and thus, you need to have a target list of most effective algorithms.
When you go deep down into the subject of algorithms and start using libraries and tools, it becomes exhausting to learn about each and every possible algorithm.
Creating a Target List
Creating a target list is an easy task, yet very effective in improving your working efficiency. The reason why I insist you to create a list is that many developers run into a dilemma when they have multiple options available, but not a single of them seems to solve the problem completely. It generally leads them to the following problems:
- It causes the developers to pause and dwell longer into their thoughts such that they end up doing nothing.
- It is important to find a solution to the problem before finalizing the best tactic. So, a better process with few options.
- Some of them have a tendency to overuse a few algorithms for the majority of the problem. This restricts the possibility of new achievements and most of the problems are left unaddressed.
- Avoid favorites, be open to new possibilities. Focus on the most effective solutions, and extract the possible best out of them.
It doesn’t mean you need to be an expert in algorithms. In fact, there’s hardly any need to know about every possible algorithm. You only need to collect basic information that includes algorithm names and related general problems.
Build Your Own Personal List
Building your own personal list is the foremost important step in machine learning. It’s way too simple.
- Use either a text file, word document, or spreadsheet.
- Write down the general class of algorithms, algorithm names and what sort of problems they are applicable to.
- Group them into your own categories including the details you care about.
Common Machine Learning Algorithms
Out of hundreds and thousands, these are the common machine learning algorithms:
- Decision tree algorithm
- Time series forecasting algorithm
- Deep learning algorithm
- Regression algorithm
- Feature selection algorithm
- Rating system algorithm
- SVM algorithm
And so on…
Getting Started with Algorithm Lists
It’s easy to come up with an algorithm list, but the real question is, “why do you want this list?” The question will help you customize your list to your need on the basis of algorithm properties with proper description.
Secondly, what are you currently working on? List all the algorithms related to that problem, it can be SVM, time series, image classification, and so on.
Edit the list from time to time depending on the problem and you’ll end up with the most effective list of algorithms. Sounds easy, but certainly worth it.
Algorithm lists can help you tackle innumerable problems that you might not be thinking about yet.
It is obvious for you to handle a familiar problem with a known solution, which limits the possibility of varied results you can achieve. The algorithm list can help you push you out of your comfort zone and work on different approaches. Be systematic and open, a list can be a great booster for your machine learning training.
Finding the Algorithms
You don’t need to do a heck of research and dug deeper into machine learning books and libraries. A simple online search and Wikipedia can do wonder to bring out the names of necessary algorithms. Sites like DataTau can also help you find relevant algorithms.
You don’t need a detailed description, it’s better to write the specifics, for example, transfer functions needed for a neural network or the kernel functions for a Support Vector Machine (SVM).
Just get started with the list right away, it doesn’t ask you for your excellency in academics.
Understanding Machine Learning Algorithms
Understanding an algorithm requires more than reading relevant textbooks or going through several online resources.
It is a little sad to know that most of the publications and research papers don’t provide you with elaborate details of algorithms unless you are ready to take a step forward and find the work of original authors.
Figure out the details by going through the details both by original authors as well as reliable secondary interpretation.
An algorithm is a subject of never-ending research making it sort of exhausting to ever find the right definition. The basic description of an algorithm can be carried out, in reality, through different computational implementation. It is imperative to consider an algorithm from different viewpoints to figure out the best possible solution.
Digging deeper into the Algorithms
When you work on algorithms, you realize there is more than just computation. You also need to consider meta-information, which is crucial for certain use cases.
Consider an example of a heuristic approach. Working on a summary of heuristics to receive the quick result and neglecting the bigger picture.
On the other hand, having a preplanned general dataset to solve the problem and thus, implementing the limitations on the description of the algorithm.
Information you must include in your Algorithm Description List
Consider using an algorithm description template to maintain a proper structure of your algorithm list. Also, make sure to include the following important information in your template:
- Abbreviation and standard of the algorithm
- Working strategy of the algorithm
- Goal of the algorithm
- Analogies used for the description of algorithm behavior
- Flowchart or pseudocode
- Applicable classes of problem
- Resources for learning more about the behavior of the algorithm
- Example datasets relevant to the algorithm
- Primary source of the algorithm
Researching the Machine Learning Algorithms
Algorithms are a crucial part of mastering machine learning. These algorithms are way different from sorting algorithms.
Also, they are not data-driven but are highly adaptive indeed. They are stochastic and it is almost impossible to finalize their precise behavior. As a result, it is difficult to judge their best and worst performance in different scenarios.
You need to use them, figure out their behavior all by yourself and then, understand their characteristics. Start with a systematic approach to research the algorithms. Here are a few trustworthy sources to help with your research work:
- Authoritative Sources: Authoritative sources have a standardised description of each algorithm along with expert interpretation. This includes textbooks, lecture notes, slides, academic papers and so on.
- Seminal Sources: Seminal sources are the original sources of the algorithm. These are rarely talked about as they contain theoretical and academic work rather than practical information but are always valuable to understand the roots of the algorithm by the original author. It includes conference papers, journals, and technical reports.
- Heuristic Sources: Heuristic Sources can help you deal with the specialized problems and stay up-to-date with the latest modification of the algorithms to suit the given problem. It includes question and answer websites, blog and forum posts as well as application-driven conference papers and open source projects.
Research is not only meant for academics and you also don’t need to be a Ph.D. holder or an expert to take a fine look into the concept of machine learning algorithms.