BABY STEPS TO ENTER THE WONDERFUL WORLD OF MACHINE LEARNING MOUNTAINS & DATA SCIENCE DREAM LAND

Documentation is the Mother of the Best Learning Resources in the world.

A mom holds the hands of her baby to help the baby to walk, jump & climb the ladders of hardwork & reach the heights of Happiness & Success

Similarly, Software Documentations hold the hands of beginners to advanced learners to create amazing Software

Bonus tip : Documentations are open sourced(free resources)

Hello , Hope you are doing good.

From the bottom of my heart, I would like to Thank the Intelligent Inventors & Developers of the Open Source Softwares & the supportive knowledge-sharing Open Source Community

I will use the following shortcuts in this article:

ML = Machine Learning

DS = Data Science

Okay let’s see how to enter the Wonderful World of Data Science Dream Land & Machine Learning Mountains

STEP-1 :PROGRAMMING LANGUAGE

Humans can speak to other human in various languages

Eg: English, Arabic, German, French, Chinese, Korean, Tamil, Telugu, Hindi, Japanese, etc.

Similarly Humans can speak to computer in various languages called programming languages.

Eg: Python , R , C ,C++ , Java, Julia, Kotlin, Sql, etc.

In order to communicate with a machine , it is necessary to know atleast one language which the computers (machines) can understand.

From my experience, I would suggest you to choose to learn Python first as it has a simple, easy to understand Syntax.

The first step to learn DS/ML is to learn Python.

Here is the Official Documentation of PYTHON for you to learn

STEP-2 : DATA PREPROCESSING & ANALYSIS

The real world data is ambiguous with lots of mistakes & you will come across data with many missing values.

There are different types of data ranging from text (numbers & alphabets/strings), image, audio ,video, gif, etc.

The data must be cleaned & converted to a certain standard format, in order to get some meaningful information from the data. This process is called Data Preprocessing and involves many sub steps

Our companion Python makes it easy for us to preprocess the data by providing some amazing libraries like Numpy & Pandas. You can install & import these libraries just by using 2 lines of Python code

Eg:

import numpy as np

import pandas as pd

The second step to learn DS/ML is to learn Numpy & Pandas

Here is the Official Documentation of NUMPY for you to learn

Here is the Official Documentation of PANDAS for you to learn

STEP-3: DATA ANALYSIS

We must do a detailed analysis of our data to determine what type of relationships do the columns have with each other, how they affect prediction, what is their significance in the desired output ?

Eg: We will be analysing the correlation maximum value, minimum value, how frequent the values get repeated & what is their average, etc.

Don’t worry, our Comrades Numpy & Pandas have some amazing pre-written codes to do a detailes analysis of data.

BONUS TIP: Did you know that Pandas can process millions of rows of data ? Surprising ... isn’t it …?

The third step to learn DS/ML is to learn Numpy & Pandas again in detail

STEP- 4 : DATA VISUALIZATION

Visualization is an art of presenting graphical version of data & helps in better analysis. Visualizing is more easier than going through the whole bunch of data & also makes easier to present our views & insights to other people.

We will be visualizing our data using graphs & plots like bar plot, pie chart, histogram, line chart, etc.

Don’t worry as our open source community has already created ready to use visualization libraries like Matplotlib, Seaborn, Bokeh,etc. By importing these libraries, we can create amazing stunning Visuals & plots with just a few lines of code

Here is the Official Documentation of MATPLOTLIB for you to learn

Here is the Official Documentation of SEABORN for you to learn

STEP-5 : BUILDING A MACHINE LEARNING MODEL

Our data is ready… Hmmm… Next comes the most important step of Machine Learning. We will build one or more Machine Learning Models to extract patterns from data. ML models will help the Machine to learn useful things from the data. Once it learns everything, our machines will complete almost all the difficult tasks assigned to it which are highly impossible to be done by human beings.

There are several ML models like Regression, Classification, Random Forests, Naïve Bayes, Decision Trees , etc. Consider yourself lucky because some kind hearted brilliant developers have already built these models & have open sourced them (made them freely available to use). You can easily import all the models from their respective modules of the Machine libraries such as scikit-learn, Tensorflow, Pytorch, Theano, etc. You can learn these libraries one by one.

If you are a beginner, I would suggest you to give a jumpstart by learning scikit-learn as it is simpler & easier compared to other libraries.

Here is the Official Documentation of SCIKIT-LEARN for you to learn

Here is the Official Documentation of PYTORCH for you to learn

Here is the Official Documentation of TENSORFLOW for you to learn

BONUS TIP:

For ML Start with Scikit-Learn. As you start learning Machine Learning Deeply, you will discover another wonderful world of Deep Learning. Then for next step you can choose either Pytorch or Tensorflow.

For easier learning & concise code choose Pytorch.

For production level code, choose Tensorflow

STEP-6: EVALUATION

All done. What next…? Its time to check if the models we built are working correctly & do they serve the intended purpose? Are there any problems or errors in our data or code or models ? We evaluate the models using some measures like Accuracy, Precision, Recall, Confusion matrix, etc. After evaluation, if our models don’t meet the desired requirements, we repeat some of the above steps & adjust our existing model or choose a different model & achieve the desired outputs. Our Champion scikit-learn will do all the hardwork for us & help us in evaluating our models with its awesome built-in functions .

Thus learning the open source Machine learning libraries from their freely available documentation websites makes our life easier & more productive. I hope this article paves a way for you to enter the world of Data Science & Machine Learning by using the freely available resources contributed by our Fantastic Open Source Community of Developers

Thank you for spending your precious time to read this article.

This is my first article. I warmly welcome your suggestions & feedbacks. Thanks in advance !

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