Basics of Machine Learning
- Technology Spiritual
- Aug 6, 2020
- 3 min read
Updated: Aug 7, 2020
Machine learning is an application of artificial intelligence (AI) which provides systems automatically learning and improve themselves from experience without programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning start with observations or data, like direct experience, or instruction, in order to look for patterns in data and make decisions in the future base. The primary goal is just computer’s automatic learning.
Machine Learning Methods and algorithms
Supervised machine-learning algorithms.
Unsupervised machine learning algorithms.
Semi-supervised machine learning algorithms.
Re-enforcement machine learning algorithms.
Later on, I will share a brief introduction about these algorithms with you for more understanding.
Now “why Python is best for Machine Learning and Data science projects”.
Now next thing is about Python language, if you want to work on Machine learning or data science the best programming language is python. Why python is best for machine learning? I have the answer to this question. There are several things, which makes it special.
Python is Universal Language: This means to say that we can develop anything like website, applications, OS depended execution programs etc.
Python is very easy to learn: Yes! This is true; python is very easy to learn. I am not from programming background but still I am working on python.
Rich Library System: A rich library system is making things easy and quick. All necessary stuff has already developed in these libraries.
So here, I am introducing an environment for python, where you can easily run and compile your python code. The distribution called “ANACONDA”. Anaconda provides you all important and required platform for python development. You can install and run your first program by using below mentioned links.
For Setup your personal LAB
Create your first Webapp
Exploratory Data Analysis (EDA)
Before working on Machine Learning models or projects, firstly you have complete your EDA part every time. This can take your 60% of time to be completed. It is most difficult and challenging part in machine learning.
In EDA, we just explore the given data and analyze that data for further use. Here you have choose specific data from all given data first, it is also called “Training Set”. Means to say a select particular rows and columns for future use. To make this done python has few basic libraries, which will useful for your projects.
These are:
Numpy
Pandas
Matplotlib
Seaborn
Sciklearn
There other libraries also but majorly these are in market trends. Here I will going to explain bit about these libraries, which will give an idea to use these libraries.
Numpy: This is use for mathematical calculations, Manage your data array and you can manipulate the data easily.
Pandas: This used to represent your data with row and columns. Like excel or other similar tools.
Matplotlib: This used for visualization purpose. Once you have select your specific data than you have to visualize this for better understanding.
Seaborn: Same as Matplotlib, it is for data visualization.
Sciklearn: This is for machine learning concept; with the help of Sciklearn, you can design your core part of project. Means, what you can do with data for decision-making, and predictions.
In next blog, I will share:
How to use Jupyter notebook for Machine Learning and Data Science.
Few basic things about Jupyter Notebook to become familiar with basic terms.
Different Data types like List, Dictionary, Tupple
Various useful function in Pandas Library.
We with me to become expert in Data science and Machine Learning. Thanks for reading.
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