Skip to main content

How to be expert in Machine Learning

 

Image for post

One should know how to play with data

First of all the main thing, we need to be focused on before stepping into machine learning is that we should know how to play with data. Because believe all the geeks sitting out there doing machine learning and other cool stuff is all because they know how to play with data.

Note: Hereby playing with data means is you should know how to manipulate the data or reshape the data into something that model can understand and make sense from it and able to predict the new value.

So what one fresher or maybe who know something but not pro should need for playing with data is only some tools like

Pandas — For Python

By pandas, one can do anything that he/she can do with data. literally anything.

Applying ML Models

After you need the data you wanted to feed the model you will have to do only 2% of the work you did for reshaping the data. Because now you have to use 2 golden words of machine learning FP

.Fit()

.Predict()

It will be going to some for any model you use just import and use these two functions for train and predict.

So to become an ML expert you need to know data manipulation only rest is just 2 golden words.

Comments

Popular posts from this blog

NEW TREND OF DATA SCIENCE: REINFORCEMENT LEARNING

Reinforcement Learning (RL) is a machine learning method that empowers a specialist to learn in an intuitive environment by performing trial and error utilizing observations from its very own activities and encounters. In spite of the fact that both direct and reinforcement learning use mapping among input and output, not at all like supervised learning where input gave to the specialist is basically the right set of activities for playing out a task, reinforcement learning utilizes prizes and discipline as signs for positive and negative conduct. When compared with unsupervised learning, reinforcement learning is distinctive as far as objectives are taken into consideration. While the objective in unsupervised learning is to discover synonymities and contrasts between data points, in reinforcement learning the objective is to locate a reasonable activity model that would boost the aggregate total reward of the specialist. Reinforcement learning will be a huge thing in Data science in ...