Top machine learning tricks

Machine learning, artificial intelligence, and big data are the buzzwords of the digital world. For a very long time, we have been using machine learning technology without actually realizing it. Every online or app recommendation we get while searching on the internet or smartphone is backed by machine learning. Data science on the other hand covers a wider spectrum of domains and one of them is machine learning. After realizing the potential of big data, data science merged as a field that utilizes algorithms and mathematical calculations to reap business insights. Data science professionals draw meaningful information from a huge volume of data. Although it is a time-consuming process, data scientists are single-handedly engaging in them to address business concerns. But things could change in the near future. The infusion of automation and intelligent machines can take over a certain level of data science works. To obtain complete success, professionals and data science students should work along with machine learning to accelerate data analysis and segmentation. Therefore, data scientists should understand top machine learning tricks and use them in practical implementation. This can help machines take the right decision. IndustryWired has listed top machine learning tricks that data science students should learn in 2021. 

Top Machine Learning Tricks

Ease the Burden of Data Formatting 

Gathering the right data and formatting it with uniformity is a troublesome process. But it is necessary to obtain data-driven insights and answers. Therefore, data scientists should use machine learning to prepare a training dataset that could avoid repetitive mistakes. It can help review the data and build effective models.

Keep an Eye on Transfer Learning

Besides being an expert in data science tactics, data science students should also keep a close eye on other disruptive technologies like transfer learning. Transfer learning means reusing parts of a neural network trained for a similar application, instead of training the neural network from scratch. This will help them gear up their machine learning game.

Use Different Modeling Algorithms

Using a single modeling algorithm to abstract insights is not a clever move. It can sometimes pave the way for complex issues like discrimination and bias. To counter this, data science students should construct a biased training dataset by oversampling or undersampling. By doing so, the trained data will be out of bias.

Understand Where Machine Learning Fits

If you are a data science student planning to use machine learning to its fullest, the first thing you need to do is centralize your data science motive with machine learning operations. Many business organizations already use data science and machine learning together to unravel profitable insights in sales, HR, finance, and marketing. Therefore, as a data science professional, you should be willing to explore your aspects of machine learning as well.

Build a Machine Learning Training Ecosystem

Even if you are a data science student or a professional, you can still learn machine learning. Although there is no 100% assurance that the machine learning knowledge will help you big time, you can find a connection between data science and machine learning that others lack. Therefore, constantly try to apply machine learning tactics in your data science projects to ensure it is effective and provides correct solutions.

Add Training Data to the Machine Learning Model

Data science students can infuse their training data on machine learning to help derive a better fitting model. For example, use customer data to predict the outcomes easily. A machine learning model can acquire insights faster than humans. Therefore, they can aid in building propensity or the next best offer models.

Educate the Executives on ML Advantages

Although organizations know that machine learning is an amazing technology that could help them with training the datasets, little do they show interest in it. Many companies that have a big data science team barely have one or two employees working on machine learning. Therefore, promote machine learning to the executives and find ways on how other data science professionals can learn the technology to perform better.