Traditional Programming vs Machine Learning
Traditional computer programming has been around for more than a century, with the first known computer program dating back to the mid 1800s. Traditional Programming refers to any manually created program that uses input data and runs on a computer to produce the output.
But for decades now, an advanced type of programming has revolutionized business, particularly in the areas of intelligence and embedded analytics. In Machine Learning programming, also known as augmented analytics, the input data and output are fed to an algorithm to create a program. This yields powerful insights that can be used to predict future outcomes.
Difference Between Traditional Programming and Machine Learning
The difference between traditional programming and machine learning lies in their approaches to problem-solving and how they are programmed to handle tasks:
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Approach to Problem Solving:
- Traditional Programming: In traditional programming, a programmer writes explicit rules or instructions for the computer to follow. These rules dictate exactly how the computer should process input data to produce the desired output. It requires a deep understanding of the problem and a clear way to encode the solution in a programming language.
- Machine Learning: In machine learning, instead of writing explicit rules, a programmer trains a model using a large dataset. The model learns patterns and relationships from the data, enabling it to make predictions or decisions without being explicitly programmed for each possibility. This approach is particularly useful for complex problems where defining explicit rules is difficult or impossible.
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Data Dependency:
- Traditional Programming: Relies less on data. The quality of the output depends mainly on the logic defined by the programmer.
- Machine Learning: Heavily reliant on data. The quality and quantity of the training data significantly impact the performance and accuracy of the model.
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Flexibility and Adaptability:
- Traditional Programming: Has limited flexibility. Changes in the problem domain require manual updates to the code.
- Machine Learning: Offers higher adaptability to new scenarios, especially if the model is retrained with updated data.
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Problem Complexity:
- Traditional Programming: Best suited for problems with clear, deterministic logic.
- Machine Learning: Better for dealing with complex problems where patterns and relationships are not evident, such as image recognition, natural language processing, or predictive analytics. Learn about What is predictive analytics.
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Development Process:
- Traditional Programming: The development process is generally linear and predictable, focusing on implementing and debugging predefined logic.
- Machine Learning: Involves an iterative process where models are trained, evaluated, and fine-tuned. This process can be less predictable and more experimental.
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Outcome Predictability:
- Traditional Programming: The outcome is highly predictable if the inputs and the logic are known.
- Machine Learning: Predictions or decisions made by a machine learning model can sometimes be less interpretable, especially with complex models like deep neural networks.
Predictive vs. Augmented: Analytics Strategies for the Future
Download NowHere’s a closer comparison of traditional programming versus machine learning:
Traditional Programming
Traditional programming is a manual process—meaning a person (programmer) creates the program. But without anyone programming the logic, one has to manually formulate or code rules.
In machine learning, on the other hand, the algorithm automatically formulates the rules from the data.
Machine Learning Programming
Unlike traditional programming, machine learning is an automated process. It can increase the value of your embedded analytics in many areas, including data prep, natural language interfaces, automatic outlier detection, recommendations, and causality and significance detection. All of these features help speed user insights and reduce decision bias.
For example, if you feed in customer demographics and transactions as input data and use historical customer churn rates as your output data, the algorithm will formulate a program that can predict if a customer will churn or not. That program is called a predictive model.
You can use this model to predict business outcomes in any situation where you have input and historical output data:
- Identify the business question you would like to ask.
- Identify the historical input.
- Identify the historically observed output (i.e., data samples for when the condition is true and for when it’s false).
For instance, if you want to predict who will pay the bills late, identify the input (customer demographics, bills) and the output (pay late or not), and let the machine learning use this data to create your model.
In summary, traditional programming is rule-based and deterministic, relying on human-crafted logic, whereas machine learning is data-driven and probabilistic, relying on patterns learned from data.
As you can see, machine learning can turn your business data into a financial asset. You can point the algorithm at your data so it can learn powerful rules that can be used to predict future outcomes. It’s no wonder predictive analytics is now the number one capability on product roadmaps.
The Definitive Guide to Predictive Analytics
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