How to Implement the machine learning models by using python libraries
Machine learning is the scientific study of algorithms and a data analytics technique that teaches computers to perform a specific task effectively without using explicit instructions. Anyone can learn the basics of machine learning using an approachable, and well-known programming language, Python. In order to learn firstly, you will be learning about the purpose of Machine Learning and where it applies to the real world and secondly, you should get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
Machine learning algorithms are used in a wide variety of applications such as
- Email filtering
- Detection of network intruders
- computer vision
- Health care
- Telecommunications and so on
Purpose of machine learning
- To allow the computers to learn automatically without human intervention or assistance and adjusts the action accordingly.
- To identify important insights in data and prevent fraud
- Help investors to know when to trade.
Machine learning model
The machine learning model is exposed to a large number of inputs and also supplied the output applicable to them. It tries to figure out the relationship between input and the result. The prerequisites are that which the readers need to be able to create machine learning models. After that train them and then use them to predict the result in python. There are three types of machine learning models:
- Binary classification
- Multiclass classification
Three basic types of machine learning algorithms
- Supervised learning
- unsupervised learning
- Reinforcement learning
There are a few libraries and resources that will be used. Some are:
- Pickle: This is a native python library to save serialize and load de-serialize python objects as files on the disk
- Flask: Flask is a python based easy to use web framework.
- Pythonanywhere: It's free to use educational website that allows hosting python flask. It provides a complete python development environment.
If you want to master machine learning with python you have to know about the basic Python skills, the foundation of machine learning skills, scientific Python packages overview.
Training and deploying machine learning model using python libraries
Here you will get the idea to use python libraries to create a proper machine learning back end. Following steps should follow:
Getting a dataset
Finding a good data set is the first and major requirements. We cannot make accurate predictions if the dataset is bad, or too small. You can find some good datasets at Kaggle or the UC Irvine Machine Learning Repository.
Training a model
In machine learning, there is a relationship between a label and its features. You can do this by showing an object (your model) a bunch of examples from your dataset. Each example helps define how each feature affects the label and this is referring as training your model. You can use the estimator object from the Scikit-learn library for simple machine learning. Estimators are empty models that create relationships through a predefined algorithm.
Importing and exporting our Python model
The pickle library makes it easy to serialize the models into files that you create. You'll also able to load the model back into the code. This will allow you to keep the model training code separated from the code that deploys the model.
Creating a simple web-server
At first, you have to create a server to deploy your model. Servers listen to web traffic. It runs functions when they find a request addressed to them. The function that runs can depend on the request's route and other data that it has and then the server can send a message of confirmation back to the requester. If you want to create web servers in record time you should use the flask python framework
Adding the model to the server
Finally, with the pickle library, you'll be able to load your trained model into the web-server. You can access it by sending a post request to your "/echo" route. After that, the route grabs an array of features from the request body and gives it to the model. Then the model's prediction is sent back to the requester.
Here you'll get the top 5 books for machine learning with python
There is a wide variety of applications of machine learning. Machine learning can create thousands of model in a week using python libraries. This article shows you the step by step path to implement machine learning using python libraries