Step by Step Guide to Machine Learning

Data Science, AI and ML
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Machine Learning is everywhere, but what is the actual meaning of Machine Learning. So, here is my take on Machine Learning Introduction. There are two words, Machine + Learning, Machine means Computers, but what is learning? The term “Learning” means, the acquisition of knowledge through study, experience, or being taught/ training, therefore Machine Learning simply means, the acquisition of knowledge through study, experience, or being taught/ training of a Computer.

Why Machine Learning?

The main difference between traditional programming vs Machine Learning is the way and power of thinking. In traditional programs, we provide rules and datasets as inputs and we get answers. But, in Machine Learning we have to provide the datasets and the answers, the model will provide us the Rules.

Suppose, take X as inputs like, X = {1,2,3,4,5}, and Rule Y = X + 3. Then as an answer we got this, Y = {4,5,6,7,8}. But, if I ask you to find the relation between X and Y, where X = {0,1,2,3,4} and Y = {-2,1,4,7,10}. Find the answer in Comment Section.

That’s why we need Machine Learning, with the help of Artificial Neural Network we can find the answer really quick.

Machine Learning Introduction

In this era of Deep Learning, Machine Learning is a subset or sub-field of Artificial Intelligence. We may think as a family structure like Artificial Intelligence is the Grandfather, Machine Learning is being the Father and Deep Learning is the Son/Daughter.

Machine Learning is basically studying computer algorithms that can improve automatically with the help of experience, knowledge. It tries to find some information and patterns in the input data, it learns from it and next time when it sees that kind of patterns in a dataset, it provides higher accuracy.

Steps to Follow

Machine Learning has 4 main steps, that you have to follow.

  • Data Collection : Data comes from a variety of sources. It may come in text format as well as image format. You have to select the correct data for your Machine Learning model.
  • Data Preprocessing : After collecting or storing the data, preprocess the data may help you to get better accuracy. Sometimes, the dataset may contain a null value, missing value, wrong value, etc. So, we need to preprocess the data into a format that the Machine Learning algorithm can work with it. So, this step is very much important.
  • Training : This is the part where we have to train our model with training data, the computer can understand only numbers, but the training data may be in text or images format. We have to give a valid format to the model to train. And that’s why the Data Preprocessing is a vital step. Here we have to train our model with some numbers of epochs. In this stage, the model learns the features or finds some patterns into the training data. So, that whenever it gets some related data, it should be able to predict that correctly, well, neural networks or machine learning may not be right all the time. That’s why we have to train the model with proper data and reasonable times of epochs.
  • Testing : After training the model or learning all the features correctly, the model is ready to face data which was not in the training dataset. Here we evaluate the model performance over test data. But remember, the testing accuracy should not be greater than the training accuracy. If you get a test accuracy which is greater than the training accuracy, then your model might end up with Underfitting.

Conclusion

Sometimes you get demotivated thinking about, is it too late to learn ML, the answer is No. Stop overthinking, be on the right path, and let’s do it. Keep your mind fresh. The fact is ML is a huge field, so give it some time to understand properly.

Best wishes to you all!