Difference between machine learning Algorithms and Traditional Algorithms

Traditional Algorithms are designed for problems which we have already know how to solve and so we explicitly program a computer to solve them.

While machine learning Algorithm deals with a problem without clear-cut steps towards a solution.

The output of a machine learning Algorithm varies with the hyperparameters such as training data, testing data, training time, learning rate, optimizer, epoch etc.

Machine Learning in layman definition: is the ability of a system to learn and improve from experience (data) without being explicitly programmed. or is the way to make computers learn how to perform complex tasks whose processes cannot be easily described by humans.

Machine learning Algorithm is the ability of a computer program to search for patterns in data to make better decisions or a set of procedures that help a model adapt to the data given or specifies the way the data is transformed from input to output and how the model learns the appropriate mapping from input to output.

Machine Learning Techniques/Style learning style is the ways an algorithm model a problem based on its interaction with the experience or environment. Learning Style an Algorithm can Adopt is Supervised Learning, Unsupervised learning, Semi-supervised and Reinforcement Learning.


List of Most used Algorithms Continue reading “Difference between machine learning Algorithms and Traditional Algorithms”

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Artificial Intelligence for Business


There are tremendous applications of AI for business growth, ranging from chatbots for customer services to data analytics for making a predictive recommendation such as Amazon recommendation systems on amazon e-commerce, google email auto-completion, facebook user suggestions etc.

Many businesses are having a hard time to Integrate Recommendation systems and it’s associate into their existing services as a result of low or no data.

Leveraging on Transfer-Learning, many businesses can have a good model  Integrated into their services with a little amount of data, high model accuracy, reduce cost, and less model training time.

Internet of things devices has enormous potential for generating real-time data, clean and accurate data for model training which will in effect increase model score or accuracy, despite these advantages data generated by IoT devices are usually small as a result of latency and other factors. Leveraging on Transfer Learning business make effective use of data generated by IoT devices for Business Intelligence.


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