How to start with Machine Learning and Data Science ?
Getting started with Machine Learning and Data Science
If you are a beginner and you are planning to learn machine learning and data science in the future so this article would guide you on how to start with machine learning and data science.
in this article, I would tell you about give some information about
how to start with machine learning from scratch if you are planning to learn machine learning.
I would give a complete roadmap for machine learning and how to start with machine learning if you want to learn.
I am considering you a beginner for machine learning and I will guide you
from where you can start and what is the strategy to excel the course.
How to start ?
first thing first we need to learn a programming language
python is among the best-suited programming language for machine learning and data science.
why Python?
Python is easy to learn and it is a powerful programming language
Python, as a full-fledged programming language, is an excellent tool for developing algorithms for production usage. Python has a number of libraries for basic data analysis and machine learning.
Data Science
Getting fresh results from data is important to data science.
Data science enables us to extract meaning and information from massive amounts of data. There is a lot to learn from processing the massive amounts of raw data kept in data warehouses.
Here are some branches of Data Science.
Data Visualization
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps.
Data Analysis
data analysis is used to analyse our data cause in real-life data is not easy to gather and sometimes we need to make our data more efficient to improve our model performance ,accuracy and to gain some useful information from data.
Data Cleaning
in the real world, data gathering is very difficult and sometimes data can be messier so we need to clean the data first to improve our model precision and accuracy.
Data Mining
data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data.
Machine Learning
Machine learning is a data analysis technique that automates the creation of analytical models. It is a subfield of artificial intelligence that is predicated on the premise that systems can learn from data, spot patterns, and make choices with little or no human interaction.
In machine learning, there are 4 types of learning.
Supervised learning
Supervised learning involves learning a function that maps an input to an output based on example input-output pairs.
Unsupervised learning
Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes.
Reinforcement earning
Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation.
Semi-supervised learning
Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.
Deep Learning
deep learning is a branch of machine learning that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
Neural Networks
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
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Conclusion
well I have good news for you I would be bringing some more articles to explain machine learning concepts and models with codes so leave a comment and tell me how excited are you about this.
