Getting Started With Machine Learning Using Python
Getting Started With Machine Learning Using Python
In this article, take a look at getting started with machine learning using Python.
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What Is Machine Learning?
Machine learning is a part of Artificial Intelligence that enables computers to learn automatically and improve themselves through experience. The primary focus of machine learning is to develop computer programs that could improve themselves according to the newly discovered data without being explicitly programmed. It predicts an output by combining data with statistical tools. It is also related to data mining and Bayesian predictive modeling.
A system receives data as input and uses the algorithm to provide an output. The machine learning is used to fraud detection, portfolio optimization, predictive maintenance, and so on. There are several machine learning algorithms such as Naive Bayes, Decision trees, Support vector machine, K-nearest neighbor, K-means clustering, Random forest, etc. Today, it is being used in price prediction, self-driving cars, fraud detection, and even natural language processing.
Machine learning can be categorized into three parts:
- Supervised learning: In supervised machine learning, the machine learns from well-labeled data that helps to provide the correct output.
- Unsupervised learning: In this algorithm, the machine is trained using non-categorized data, and it acts without guidance. This algorithm is used to figure out the clustering of the input data.
- Reinforcement learning: It allows the computer program to interact with its dynamically, and the program receives positive or negative feedback for improving the performance.
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What Is Python?
Python is a high level, object-oriented programming language, and it was developed by Guido van Rossum in 1991. It is simple to understand and also easy to learn. It boosts program modularity and code reuse. As it is an interactive language, which means we can directly interact with the interpreter to write code.
Why Use Python for Machine Learning?
There are various features of Python that make it high technology for ML. Such are given below:
- Easy to code: It is very easy to write code in Python as compared to another programming language like C++ and Java.
- Object-oriented: Python is completely OOPs based language. It supports all concepts of object-oriented language like classes, objects, inheritance, polymorphism, encapsulation, etc.
- Integrated: It can be easily integrated with other languages like C, C++, etc.
- Dynamically: It is a dynamically typed language that means do not need to declare the data type because it is decided the type of variables at run time.
- Portable: Python is an independent language. You can execute the same program on any OS Windows or MAC, as you do not need to write different code while running on another operating system.
How Does Machine Learning work?
The process of machine learning starts with entering the training data into the selected algorithm. The training data may be known or unknown is used to develop the final Machine Learning algorithm, and the type of training data makes an impact on the algorithm.
To check this algorithm is working properly or not, the new data is entered into the machine learning algorithm, then the results and prediction are checked. If the result is not expectable, so re-trained the algorithm multiple numbers of times until the desired output is not provided. It enables the Machine Learning algorithm to continuously learn by themselves and produce the optimum result that increases the correctness over time.
Applications of Machine Learning
There are several applications of Machine learning:
- Google translate: Machine learning is widely used in Google translation. It is one of the most powerful applications of Machine learning. The GNMT (Google Neural Machine Translation) is a neural machine learning of Google that works on many different types of languages and dictionaries by using natural language processing and provide the most optimum answer of any words or sentences.
- Self-driving cars: Machine learning plays a vital role in self-driving cars. A car manufacturing company Tesla is working on a self-driving car. The primary task of the machine learning algorithm in the self-driving car is the continuously translation of the surrounding environment and predict the possible changes to those surroundings. It mainly focuses on object detection, object localization, object classification, and prediction of movement. The unsupervised learning algorithm is used to train the models of this car to identify the object and people while driving.
- Fraud detection: Fraud detection is one of the most significant applications of machine learning. It provides security for the online transaction. Due to the availability of various online payment methods like credit or debit cards, net banking, smartphone, UPI, and several types of wallets, online transactions have increased tremendously in the past few years. Furthermore, the number of criminals is increasing to find loopholes in the online payment system.
Whenever we use any online payment method, so Feed Forward Neural network detect whether it is authorized or unauthorized transaction, and make the online transaction more secure.
- Social media: The machine learning provides the automatic friend tagging suggestions in social media applications like Facebook, Twitter, Instagram, or any other social media applications. For example, Facebook regularly notices the friend profiles that you connect with, your interest, workplace, the profile that you visit frequently. Accordingly, it suggests a list of friends on the basis of your interaction with other people on Facebook, and thus you can send a friend request to any of them if you think that person can be your friend. Furthermore, machine-learning allows Facebook to find automatically face detection and image recognition of the person which matched its database and suggests you tag with that person.
- Search engine: Machine learning is used in Google and other search engines for improving the search results. Whenever you search something and open the top link from the searched result and stay on that web page for a long time, so search engine understands that the provided result is appropriate according to the query. Equivalently, if you reach the second, third, or another page but do not open any of the pages, so the search engine assumes that the displayed result did not match according to requirement. Thus, the algorithm works in the back-end to improve the search results.
- Email spam and malware filtering: Several techniques are available for spam filtering in machine learning. Whenever you receive an email, an email is filtered as normal, important, and spam automatically. The important emails are received in the inbox with an important symbol and spam emails in spam box with the help of machine learning. The algorithms, such as Decision tree, Multi-layer perception, and Naïve Bayes classifier are used for malware detection and email spam filtering. Gmail used some spam filters such as header filter, content filter, permission filter, rules-based filters, general blacklist filters.
The Lifecycle of Machine Learning
The life cycle of machine learning is referring to collect knowledge from data. It uses data as input while having the ability to learn and improve using algorithms. It has three phases pipeline development, training, and inference. As shown in the below picture.
There are so many steps in the machine learning lifecycle. Such are given below:
- Collection of data: The collection of data is the first step of the machine learning life cycle. The objective of this step is to recognize and receive all data related to problems. The sources of gathering data can be the internet, files, database, or mobile devices. With the help of efficiency of the output, it determines the quality and quantity of the collected data. It involves the task procedure as recognizing several data sources, gathering data, and combine the data which is received from different sources.
- Data arrangement: This step arranges the collected data for further moves. It keeps the data into the appropriate location and organizes the data to use in machine learning training. The data arrangement follows two steps, which are given below.
- Data analysis: It is used to determine the quality, characteristics, and format of data.
- Data pre-processing: It is used to preprocessing the analyzed data.
- Data wrangling: In this step, cleaning the data and convert it into the operational format to make it more appropriate for analyzing the data. Sometimes, the collected data is not useful. It comes with various issues. It can be invalid data, missing values, duplicate data, and noise, etc. So, we have to use several data filtering techniques to clean the data.
- Data analysis: It is used to build a machine learning model using various analytical techniques to analyze and review the outcomes. It determines the nature of the problems where we select the machine learning techniques such as regression, classification, association, cluster analysis, etc. then construct the model using analyzed data, and evaluate the model.
- Train the model: In this step, the model is trained using several machine learning algorithms to improve its performance and to get better output. The purpose of training a model is that can understand the different patterns, features, and rules.
- Test model: After training a model, it goes to the testing phase for checking if the model is providing the optimal result or not. It is analyzed the percentage accuracy of the model according to the requirement of a project or problem.
- Deployment: In the last step deployment, we establish the model in a real-world system. If the trained model is producing a correct answer according to the requirement with less time, then we deploy the model in the real system. But if it is not providing the accurate result as per the requirements so re-train the model until it does not give the desired result.
Published at DZone with permission of Mahesh Sharma . See the original article here.
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