Machine Learning Using Python
Machine Learning Using Python
Introduction The best subject of all the Artificial Intelligence domain is machine learning which has been in the news for quite some time. This field has the potential to provide a better and necessary opportunity. It is very easy to start a career even if you have zero in Mathematics or Programming. Even if you have experience, there is no problem because it is the most important element for your success, purely to help you learn those things. Data for which you have your interest and inspiration. If you are new then you do not know where to start studying and why you need machine learning and why it is gaining the most popularity, then you have come to the right place to get better knowledge from here. I have collected better information and useful resources to help you complete all your projects.
Why Starting With Python?
If you aim to become a better and successful coder then you have to keep many things in mind but it is better to master the coding language for machine learning and data science and to use it with confidence then calm down, You don't have to be a programming genius.
And
If you need machine learning and data science, Python is a better option for those who want to start and get started. It is a minimal and intuitive language that reduces the time to get all your results and if you want then R language Can consider but all uses have been greatly influenced by Python.
Step 0. Brief Overview of ML Process You Need to Know It is learning based on its own experience. It is like a person who learns through the observation of seeing others, plays like a computer that can be python programmed through information. They are trained. Has the ability to recognize the characteristics of all elements.
MACHINE LEARNING
UNSUPERVISED LEARNING Group and interpret data based only on input data
CLUSTERING
SUPERVISED LEARNING Develop predictive model based on both input and output data
CLASSIFICATIO N
REGRESSION
Stages of Machine Learning •
Data collection
•
Data sorting
•
Data analysis
•
Algorithm development
•
Checking algorithm generated
•
The use of an algorithm to further conclusions
Data Collection
Data Pre - Processing
Data Collection
Model Evaluation
Model Training
Improving the Performance
Data sorting
02 01
06
04 03
05
08 07
Data Analysis Data Analysis Process
Data Design, Experiments, Formulating Business Problem
Data Analysis, telling Stories
Machine Learning Algorithms
A.I.
Algorithm Development
Checking Algorithm Generated
The Use of an Algorithm to Further Conclusions
To Look for Patterns, Various Algorithms are Used, Which are Divided Into Two Groups 1. Unsupervised Learning
2. Supervised Learning
Unsupervised Learning
In untrained learning, your machine receives a set of all input data that determines the relationship between the machine's data and other imaginary data. Unsupervised learning means that the python computer program itself will find new patterns and relationships between all the different data sets. Unsupervised learning can be further divided into two parts.
Supervised Learning
It shows computer capability by identifying all the elements based on the samples of supervised learning and the computer by identifying it improves the ability to send new data based on the data.
Supervised Learning Algorithms x
Support-vector machine
k-nearest neighbors
X<10
Y<0
Z<5 C
Y<0
A D
B
Z<5
W<0
Decision trees
Naive Bayes classifier
linear regression
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