How to start 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.
Python is easy to learn and it is a powerful programming language
Being a full-fledged programming language, Python is a great tool to implement algorithms for production use. There are several Python packages for basic data analysis and machine learning.
The central aspect of data science is getting new results from data.
Data science allows us to find the meaning and required information from large volumes of data. As there are tons of raw data stored in data warehouses, there’s a lot to learn by processing it.
Here are some branches of Data Science.
Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps.
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.
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 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 is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
in machine learning, there are 4 types of learning.
Supervised learning involves learning a function that maps an input to an output based on example input-output pairs.
Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes.
Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation.
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 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.
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.