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Home Future Technology Machine Learning 7 Machine Learning Methods Should Know Every Data Scientist

7 Machine Learning Methods Should Know Every Data Scientist

Machine learning is quickly becoming a standard-bearer for most of the tech giants and even the small and medium-scaled businesses and companies. Coupled with Artificial intelligence, ML is taking the world by a storm.

Product development is at its peak owing to the facilities and advantages of machine learning. The companies that fail to adopt this technology for the development of their products are likely to struggle in the near future to meet the demands of their customers. So, today we will look at some tips that will help you get started with computer network learning.

Get the terminology correct

Do not compare machine learning to AI (Artificial Intelligence), and do not get confused between these two. They both are very different technologies. Machine learning falls under the same umbrella as Computer vision, data science or deep learning, while AI falls under machine intelligence.

Understand the scope of Data Science

The 21st-century universe knows only one thing, and that is data. With data serving as a fuel for any kind of A.I. /M.L. venture, you must treat data like money. Therefore, you must embed data science and machine learning into every department of your company, like HR, finance, sales, marketing, etc.

Train more people in Data Science

The need of the hour is to build a data science training curriculum. Not everyone who practises data science is going to a data scientist. With that in mind, you would have to figure out how to train maximum people to get familiar with machine learning. Conducting a bi-monthly workshop via recognised data science institutes might assignment help.

Apply algorithms tactfully

Machine Learning continues to thrive on the quality of your algorithms. But, as computational and theoretical science has shown us over the years, trial and error are a fundamental part of data computation. No matter how good your algorithms are, if your system /product is meant to interact with the humans, it would need frequent adjustments, which indirectly means improving the algorithm.

There are countless resources available for Machine Learning. You can use open-source framework libraries like TensorFlow, Shogun, Caffe and several other free resources. However, do not put all the eggs in one basket and instead use multiple resources.  

Experiment with hybrid learning

A mixture of cheap and deep leaning is coined as hybrid learning by some experts. The scenario is something like this – If you take a pre-existing vision model of a computer and re-construct the top layers where decisions are made, you can go for a completely new framework in place of the existing one.

Develop supervised models and learn how to handle them

The whole intention of supervised learning is to use the algorithm to determine and estimate the mapping functions, such that the algorithm can predict the output variables for the specific data.

Meanwhile, you can use algorithms on the amount of data at your disposal to come up with valuable outputs. With that being said, you must develop a habit of handling big data systems. You must understand how to store and manage a substantial amount of data effectively and process them.

Educate the higher-ups

It is of the utmost importance to get the correct training set rather than to get a perfect data model. In order to keep the wrong data models at bay, do not let any random person handle the data. The person handling must understand the model and the hierarchy of it.

Your primary aim should be to promote Machine learning and its benefits to your higher-ups. Convince the users that machine learning operations are the key to your company’s future. Educate them about the benefits of using ML by presenting slides, statistics and focus on the cost reduction aspect of your infrastructure.

Parting Thoughts,

Machine learning is an ever-expanding field that is going to cross new boundaries every day. With every passing day, more companies are going to adopt machine learning. With applications ranging from a wide variety of day-to-day activities such as agriculture, insurance, DNA-sequencing, Machine Learning shows zero signs of slowing down. Therefore, you should be ready when your time comes to integrate it into your life. Adopt, learn and begin to use Machine Learning with your eyes set towards the future.

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