Getting started with Machine Learning (#GO-ML)

Let’s get started

The most common problem we face while starting with data science or machine learning is from where to start? Even I got stuck with the same question but with time I learned about it, its algorithm and about how it works but it was very slow. That’s the point, I was very slow. I spent a lot of time understanding it and then was pushed back since my basics were not clear. So here I m with a tutorial that can lead you to from a beginner to a pro in machine learning. I will be updating you guys with a blog and a tutorial on GitHub every week.

Introduction

Things we will be covering in the tutorial series:-

1. chapter1:- Python basics, Pandas, Numpy overview
2. chapter2:- Getting started with machine learning, its basics, types of learning
3. chapter3:- Classification
4. chapter4:- Regression
5. chapter5:- Neural Networks
6. chapter6:- CNNs
7. chapter7:- RNNs

The codes of all these will be available at Github.

Application of Machine Learning

So before we get started let’s know the application of machine learning because i truly believe that application makes understanding stronger.

1. Application in Recommender System:- One of the most common applications of machine learning is in the recommender system. Ever thought on how youtube gives you recommendation about some videos and you just can’t stop your nerves from watching those videos. This is because of a recommender system. They take an estimate of your watch history, your likes and dislikes in that particular field (e.g:- anime, food, traveling, tech etc) and give you a result of there recommendation.
2. Application in Image Recognition:- ok so how do i recognize my friend. I see his face and immediately my brain gives a signal that is my friend. Machine learning algorithm does similar work. It extracts features from our face and learns it(training stage).

3. Business Applications:- Machine learning can be used in business analysis work as well. In spam email detection, stock prediction. Natural language processing has proved to be very efficient in the field of machine learning.

There is endless use of machine learning. It’s heavily upon the coder how he/she implements it. Before we get started with machine learning it is also important to know the basics of python. So, I just made a tutorial which is enough to get you guys started with data science.

Note:- In Python basics, we will only be looking the things which is enough to get you started.

Get started with code

Python basics:-

There are certain simple things which we should remember before starting with python:-

1. No header files required (initially)
2. Python is one of the best examples of object-oriented programming
3. Indentation has a very important role in python

Let us see a “Hello world program in Python”

Step 1:- Open your terminal and type Python, in case you don't have python follow this link to install it on your system:- windows, linux , mac

Step 2:- If you have python installed in your system then type “python” on your terminal and press enter

Just type python on your terminal and hit enter

This is what u see after hitting enter

There we go !!!

Wait but we haven't included any header file. So are we wrong?

Well No, as discussed earlier there is no need to include any header files initially. Isn't that simple :)

This just notebook gives a brief explanation of Python basics

link to the Code:- Python Basics

Pandas

Pandas is one of the most efficient libraries for data science in Python. It makes handling of dataset very easy for the developer. The problem developers face without pandas is that it requires manually handling each and every part of the dataset like columns, rows, its size etc. But pandas has proved to be one of the most efficient libraries in the field of data science. It automates the loading of data into memory very quickly. Basically used for data manipulation and analysis. A quick overview of pandas is shown in below notebook. To know about jupyter have a look on the below notebook.

Numpy

Numpy is a library adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Now let’s talk about the best Python IDE for machine learning

1. VS code:- Visual Studio Code is a source-code editor developed by Microsoft for Windows, Linux and macOS. It includes support for debugging, embedded Git control, syntax highlighting, intelligent code completion, snippets, and code refactoring. Download it from- VS code
2. Pycharm:-PyCharm is an integrated development environment used in computer programming, specifically for the Python language. It is developed by the Czech company JetBrains. Download it from- Pycharm
3. Atom:-Atom is a free and open-source text and source code editor for macOS, Linux, and Microsoft Windows with support for plug-ins written in Node.js, and embedded Git Control, developed by GitHub. Atom is a desktop application built using web technologie. Download it from- Atom

So this was all about Chapter 1 of GO-ML tutorial. Link to the codes:- link

Thanks for your time. We hope that you liked our first part of GO-ML tutorials, follow me on GitHub for further updates on this course.

AbhishekPatnaik/GO-ML-tutorials

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