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How to start Machine Learning
How to start Machine Learning

How to start Machine Learning

Hello!! Learners,

We all want a good starting for a certain task or job and when it is important to us we can take no RISK…

In this blog we will start the journey of Machine Learning.

You can not master machine learning without practicing the algorithms yourself and also you need to go through basic of everything which might look boring but it is important to go through it.

But yes there are many resources available which will always help you to get out of any problem

So, let’s start-

1… How is machine learning useful in real world???

Some words of Jeremy Howard about Machine learning you must hear. He discuss various applications of machine learning and deep learning. He also discusses a few ways in which machine learning can impact this world and if you want more interesting stuff go to (this) video giving the concept of how to train machine to play Super Mario.

2… Language preparation -

There are many languages which provide machine learning capabilities. But R and python are the most commonly used languages and there is enough support available for both. You need to select any one of this.

My personal advice is Python because python helps me in different other projects and as they want python so I have to learn python so why not do machine learning with python

3… Statistics –

You need to have a good knowledge of descriptive and inferential statistics. Udacity offers course on descriptive statistics and Inferential statistics. Both courses will use Excel to teach basics of statistics. If you already know it than also give some time to it to refresh it.

One more thing just observe this diagram

image

You need to know the difference between all this terms, usually people get confused between these terms. I will write an article about this too.

4… Data Preparation –

There is a difference between average machine learning student and ML professional and data preparation or exploration is one of the way to differentiate them. You need to spend a very large amount of time on this but believe me this will help you in future to apply your thoughts or algorithm easily.

Try some free course for data Exploration Cleaning and Preparation for enhancing your abilities.

5… Introduction to Machine Learning -

It’s time to use the resources to go into the world of machine learning. Its better to take a free course and that course should be able to make you understand the real awesome feel of machine learning. I am giving you one name - Andrew Ng, Believe me he is the inspiration of thousands of students, the way he explained machine learning is AWESOME.

Now we can go for Machine Learning. There are many resources available to start with Machine learning techniques. I would suggest you to select one of the following 2 ways depending on your style of learning. Here is the course of Machine Learning. It is a good course for beginners and easy to understand. Professor Ng is awesome in making difficult concepts feel easy and amazing. All basic operation are covered in this course with some advance topic like neural networks. He use Octave/MATLAB instead of R or Python.

6… Time for some Knowledge competition -

Now you can take part in Kaggle knowledge competitions. Difficulty level of knowledge competition is less than prize winning challenges.

I will soon write a article upon how to start your journey on Kaggle. But you can search on google too for it till I write it.

7… Advance Machine Learning -

Now we will explore advanced machine learning techniques to understand different structure of data like Deep Learning and Machine Learning with Big Data.

Deep Learning

To help you get started below is the list of deep learning resources :

a) The most comprehensive resource is deeplearning.net. Here you will find everything related to lectures, datasets, challenges, tutorials.

b) Another course here will help will help you in Deep Learning

c) Pattern recognition using Python (Resource 1, Resource 2, Resource 3) and R (Resource 1)

d) Text Mining using Python (Resource) and R (Resource 1 , Resource 2)

Ensemble modeling

This is the difference between expert and an average professional. Ensemble can add a lot of power to your models and has been a very successful technique in various Kaggle competitions.

Machine Learning with Big Data

As data is increasing at an exponential rate but raw data is not useful till you start getting insights from it. Machine learning is learning from data, generate insight or identifying pattern in the available data set. There are various application of machine learning algorithms for example spam email detection, fraud detection and web document classification etc. Below is the list of tutorials to deal with big data using machine learning.

· Scalable Machine Learning

· Packages for Big Data in Python ( Pydoop, PyMongo) and R (Resource1, Resource2)

Now you can also participate on higher competition on kaggle.

Thanks for reading, Hope this help you.

Raj Kothari
Jul 11, 2020
ME(R/A)N | Machine Learning | Student Mentor |Mobile | Tech Writer | Learner
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