Start learning machine learning for beginner is always a challenging or may be unclear task like from where you can start machine learning, what is best online reference or material, which is best for machine learning python or R. There are lots of material available over internet from there you can learn machine learning but there are very rare website where you will get everything worth. however, i personally feel that for a beginner just keep in the mind first note down the main headings or topics of machine learning that you want to learn and search best website for each topics related to. so in this article i am going to show you what is path of machine learning.
There are following steps:
Step 1: Which is best for machine learning Python or R
Python is always good options for beginner when you are going to learn machine learning with the following benefits.
Why python is the best for machine learning
You learning a lots programming capability and easy syntax.
There are large no of community or website actively sharing machine learning content so you can easily get that.
Actively new update
You can easily get machine expert or tutor online.
Large no of packages and libraries support
API Integration is easily available.
There may be other good reason it depends on individual knowledge and use. so you can comment below if you have.
Python is also a good choice for if you are programmer or software developer or from computer science background but if you are from non-programming background and want to perform only data analytics and math related stuff and your vision in only to data visualisation and data insights nothing else then go for R. But As a student if you learn R you are going to confine or limit yourself in boundaries or only for Statistics
Step 2: Data Preparation or Exploration
To be good at machine learning or master in it. Its not about selection of good algorithm or programming language but understand data, collecting data from right source, unbiased data, cleaning data and based on data exploration you can choose fitting algorithm.
These are some best data preparation strategies to prepare good data.
Variable Identification,
Univariate and Multivariate analysis
Missing values treatment
Outlier treatment
Feature Engineering
Step 3: Introduction to Machine Learning
When you are comfortable with data handling and selection now you can learn deep dive into algorithm and functions.
There is three main task of every machine learning algorithms
Train the model
Testing the model
Predicating the model
Step 4: Advanced Machine Learning
After learning and understanding the machine learning you can explore advanced machine learning techniques like Deep Learning and Machine Learning with Big Data.
Deep Learning:
Below are the list of deep learning resources that will help you to get started:
deeplearning.net. You will find everything here – lectures, datasets, challenges, tutorials.
Another course from Geoff Hinton a try in a bid to understand the basics of Neural Networks.
Pattern recognition using Python (Resource 1, Resource 2, Resource 3) and R (Resource )
Text Mining using Python (Resource) and R (Resource 1 , Resource 2)
Ensemble Modeling
Ensembling 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
There are various application of machine learning algorithms like "spam detection", "web document classification", "fraud detection", "recommendation system" and many others.
Below are the list of tutorials to deal with big data using machine learning.