Algorithms – the word gets tossed around today for everything from your basic Instagram feed recommendations to complex interfaces like ChatGPT. But what are algorithms? Are they just for computers? How do they help you?
In today’s article, we’ll explain what algorithms are and give some examples of how you can benefit from this ubiquitous but often-misunderstood technology.
What are algorithms?
An algorithm is a set of rules or instructions to follow that help to solve a problem or complete a task. While they are used in computers perhaps most notably today, the word has its origins back in the 9th century. At its core, an algorithm has a finite number of steps, governed by rules, that explain in what order to do things. For computers, these instructions will be written in the same syntax which is a distinct structure of words and statements. That helps the computer speak that language from start to finish so the task is completed efficiently.
Are all algorithms ai-based?
Not all algorithms are based around or involve AI. Just baking a cookie involves following a set number of steps to reach a desired outcome. In fact, any prescribed method could be considered an algorithm. The steps to brush your teeth, how to solve a multiplication problem and how to parallel park a vehicle are all algorithmic. But in today’s world, when we talk about algorithms, we almost always mean the ones that are a part of machine learning and AI.
How do algorithms work?
In computer language, algorithms are a set of code which instructs a program to take a specific action to complete a task. Software using predictive analytics for sales will take a set of inputs like historical purchasing patterns, apply an algorithm and solve problems like churn, demand and other valuable outputs.
An algorithm will approach a task in one of three ways:
• Conditional – It decides on a course of action based on the rules it’s given.
• Loop – It follows a sequence over and over until the outcome is reached.
• Linear – It follows a set of statements in order to reach the outcome.
However the approach, the goal is always to arrive at the most accurate answer as fast as possible. It can do this through brute force, recursive, greedy methods, backtracking, divide and conquer or dynamic programming. These steps are either supervised, unsupervised, semi-supervised or reinforced. The level of supervision tells you how much or how little training is provided by humans before the code gets to work. Also, it’s important to note that within one piece of software, many different algorithms could be working in tandem to process the data and solve the problem at hand. They don’t have to operate in isolation.
10 Critical Machine Learning Algorithms
There are hundreds of algorithms out there from recipes for Momma’s Best Meatloaf to the code powering advanced predictive analytics for sales SaaS. In the field of machine learning, however, here are the most popular algorithms.
1. K-means – Here, the data sets are clustered away from other data in heterogeneous and homogeneous ways under an unsupervised algorithmic approach. You’ll find this one in document clustering, image compression, market segmentation and more.
2. Random forest algorithm – Where multiple decision trees are gathered, you have a forest. When a new object needs classification, each tree gets a vote and the object goes where it gets the most votes. You’ll see this algorithm in medicine, online shopping, stocks and banking.
3. SVM algorithm – The support vector machine sees us plot data across n-dimensions where n is the number of features. Then each feature gets its own coordinate so classification is easy. You’ll find it in email classification, web page classification, genes, face detection, intrusion detection and more.
4. Naive Bayes algorithm – This classifier treats a feature in one class as not related to that feature in other classes. It’s great for spam filtering, recommendations and sentiment analysis.
5. KNN algorithm – Applicable to regression and classification needs, this algorithm stores all cases and assigns new ones based on the k neighbour’s majority vote – putting it where it shares the most similarities. You’ll find this ML in image or video recognition and handwriting detection.
6. Dimensionality reduction algorithms – This category of algorithms includes MVR, decision tree, FA and random forest and it can look for related details in complex data sets across a wide range of disciplines.
7. AdaBoosting algorithm & Gradient boosting algorithm – When a huge amount of data must be processed to make accurate predictions, these two algorithms boost the efficiency of weak learners. They are applied to other ML algorithms.
8. Linear regression – This algorithm makes a link between dependent and independent variables by plotting them along a line with the equation Y=a*X+b. It can be used in predictive analytics for sales and other fields to create models.
9. Logistic regression – To estimate usually binary values, from independent variables, data is applied to a logit function. It’s often used in fraud destruction to find data anomalies.
10. Decision tree – Used for classifying problems via supervised learning, the data sets are split based on their attributes. It is used to classify data in fields like law, business, engineering and city planning.
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What are Algorithms – Summary
Algorithms have always been around us. From instructions on how to change a tyre to complex formulas in predictive analytics for sales software, the applications are endless. Anytime you need to solve a problem or complete a task, you can use an algorithm to do it. If you’re looking for the answer to your wholesale or manufacturing sales questions, we’ve got the solution. And, yep – it’s algorithms. Talk to our team today about how you can improve sales figures, cut down churn and boost efficiencies, all with our predictive analytics for sales.
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