Artificial intelligence (AI) is a huge-ranging department of computer technological know-how worried with constructing smart machines capable of completing tasks that commonly require human intelligence.

Machine learning (ML) is a branch of Artificial Intelligence (AI) and computer science which focuses on the use of facts and algorithms to imitate the way that humans study, regularly enhancing its accuracy.

Difference between AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are two very available words at this time, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. 

So I write some piece of the word for different that. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.

In short, the best answer is that: Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. And, Machine Learning is a current application of AI-based around the idea that we should just be able to give machines access to data and let them learn for themselves.

Artificial Intelligence is a technology that enables a machine to simulate human behavior.

The goal of AI is to make a smart computer system like humans to solve complex problems.

In AI, we make intelligent systems to perform any task like a human.

Machine learning and deep learning are the two main subsets of AI.

AI is working to create an intelligent system that can perform various complex tasks.

AI system is concerned about maximizing the chances of success.

The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing,     
    an intelligent humanoid robot, etc.

Based on capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI.

It includes learning, reasoning, and self-correction.

AI completely deals with Structured, semi-structured, and unstructured data.

Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.

The goal of ML is to allow machines to learn from data so that they can give accurate output.

In ML, we teach machines with data to perform a particular task and give an accurate result.

Deep learning is the main subset of machine learning.

Machine learning is working to create machines that can perform only those specific tasks for which they are trained.

Machine learning is mainly concerned with accuracy and patterns.

The main applications of machine learning are the Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.

Machine learning can also be divided into mainly three types are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

It includes learning and self-correction when introduced with new data.

Machine learning deals with Structured and semi-structured data.

In this blog, we will discuss the difference between Artificial Intelligence and machine learning.

The Difference Between Artificial Intelligence And Machine Learning

Machine learning is an application of Artificial Intelligence (AI) that gives systems the potential to routinely examine and enhance from experience without being explicitly programmed. Machine studying makes a specialty of the development of pc programs that can get admission to records and use it to study for themselves.

The process of learning begins with observations or statistics, together with examples, direct revel in, or training, to look for patterns in statistics and make better choices in the future primarily based on the examples that we provide. The number one intention is to allow the computers to examine mechanically without human intervention or help and modify movements consequently.

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.

Data scientists are specialists who excel in converting raw data into critical business matters. These scientists are skilled in algorithmic coding along with concepts like data mining, machine learning, and statistics. Data science is used extensively by companies like Amazon, Netflix, the healthcare sector, in the fraud detection sector, internet search, airlines, etc. 

Difference between Data Science and Machine Learning

Data Science is a field about processes and systems to extract data from structured and semi-structured data.

Data in Data Science maybe or maybe not have evolved from a machine or mechanical process. 

Data Science as a broader term not only focuses on algorithms statistics but also takes care of data processing.	

It is a broad term for multiple disciplines.

Many operations of data science that is, data gathering, data cleaning, data manipulation, etc.	

Example: Netflix uses Data Science technology.	

Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.

Machines utilize data science techniques to learn about the data.

It uses various techniques like regression and supervised clustering.

But it is only focused on algorithm statistics.

It is three types: Unsupervised learning, Reinforcement learning, Supervised learning.

Example: Facebook uses Machine Learning technology.

Today we discuss some differences between Data Science and Machine Learning.

Data Science VS Machine Learning

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Key Differences Between Big Data and Machine Learning

Following is the key difference between Big Data and Machine Learning:

Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other. They superimpose each other’s activities and the relationship is best described as mutualistic. It is impossible to see a future with just one of them. But there are still some unique identities that separate them in terms of definition and application. Here’s a look at some of the differences between big data and machine learning and how they can be used.

Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.

Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.

Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the relationship between existing pieces of data with the same depth that machine learning can.

Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to fed to a machine learning system in order to do further analytics for predicting output results.

Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.

Machine learning performs tasks where human interaction doesn’t matter. Whereas, big data analysis comprises the structure and modeling of data which enhances decision-making system so require human interaction.

Data Use: Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc.

Foundations for Learning: Big data analytics pulls from existing information to look for emerging patterns that can help shape our decision-making processes.

Pattern Recognition: Big data analytics can reveal some patterns through classifications and sequence analysis.

Data Volume: Big data as the name suggest tends to be interested in large-scale datasets where the problem is dealing with the large volume of data.

Purpose: Purpose of big data is to store large volume of data and find out pattern in data

Data Use: Machine learning is the technology behind self-driving cars and advance recommendation engines.

Foundations for Learning: On the other hand, Machine learning can learn from the existing data and provide the foundation required for a machine to teach itself.

Pattern Recognition: However, machine learning takes this concept a one step ahead by using the same algorithms that big data analytics uses to automatically learn from the collected data.

Data Volume: ML tends to be more interested in small datasets where over-fitting is the problem

Purpose: Purpose of machine learning is to learn from trained data and predicts or estimates future results.

Big data vs machine learning go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries while there is a lack of skilled professionals; machine learning professionals being in more demand when compared with big data analysts. When it comes to salary, both profiles enjoy similar packages and if you have skills in both of them, you are hot property in the field of analytics.

However, if you do not have the time to learn both, you can go for whichever you are interested in.

In this blog post, we will know the comparison between Big Data and Machine Learning.

The comparison between Big Data vs Machine Learning 

GitHub, Inc. is a provider of Internet hosting for software development and version control using Git. It offers the distributed version control and source code management (SCM) functionality of Git, plus its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, continuous integration, and wikis for every project. Headquartered in California, it has been a subsidiary of Microsoft since 2018.

GitHub has a great many Machine Learning Projects. Aside from being a code forming framework and capacity framework, GitHub offers a lot more things like associating individuals socially with their friends, understudies with their Teachers/Future Employers, and Developers with Technical specialists in their field. Accordingly, GitHub gives an ideal stage to programming experts, growing designers, and understudies to utilize huge loads of data accessible on Machine Learning ideas in its storehouse and foster activities in this field and offer it with the local area through GitHub. By along these lines, understudies get perceivability and get a new line of work; Developer leaps to a higher position, Professionals scale another level.

It is a part of Artificial Intelligence in which the system or model or algorithm developed by developers learns from the data supplied to it.

The data train the model without any extra coding effort.

It identifies patterns from its data and develops its own intelligence to make decisions without any human intervention.

Some of the GitHub Machine Learning Projects

GitHub hosts several projects for several use cases listed below:

This system identifies the employees as they walk past the security gate, and it can be used as attendance, obviating the need for any signature or any card punching, etc.

The application is built with several Python libraries like dlib and face_recognition.

Django framework is used in front-end development.

Capture the photos of all the existing employees at all possible angles.

Train the model with all the photos stored in the previous step.

Have the web cam installed at the gate and scan the photo of an employee.

Compare the scanned photo with stored photos and display the result.

If any new employee joins, add his photo and train the model with this.

It is an industry-relevant topic and deployed for analyzing reviews of products or services. The reviews can happen through physical or social media, and the analysis rates the reviews whether they are positive or negative. Thus, the organization can get true feedback and make a course correction in the product or service wherever it is needed.

Construct the dataset by randomly pulling out samples of reviews in various grades from a superset downloaded from social media sites

Classifier models such as Random Forest, Multinomial Naive Bayes, Linear Support Vector Classifier (SVC), and Logistic Regression are used to construct data models and validate the review data for results.

The review data from the user is taken as input, and they are validated with the data model using the relevant classifier.

The result (positive or negative) is published for all the reviews within a short time.

It helps organizations control costs and maximize the revenue from existing assets by reducing downtime and improving their performance. Predictive analytics can predict the maintenance required upfront and avoid breakdowns and predict any asset’s loading pattern or usage methods to increase the operational efficiency of any asset.

Data models with the data on the assets and the related entities will have to be built.

Models will have to be further augmented with historical data on the performance of the asset is also built.

Predictive techniques like linear regression, ridge regression, time series, ANOVA, logistic regression neural networks, and decision trees are used to predict the failures and improve the loading pattern.

Chatbots replace mundane and routine activities with an automated process, and they can be put into use in customer-facing applications for 24×7 use. In addition, it can use natural language processing to understand the language of the caller.

Develop the bot to take the queries from users and infer them.

Build the database for a bot to refer to and answer the queries.

As per the program logic, derive the answer either directly or coin the result with the data extracted from the database.

Work out a mechanism to respond to the queries of users.

Train the bot on a continual basis with different types of voices and syllables using tokenization, stemming, and lemmatization NLP techniques.

This method is widely adopted by data scientists in the stratification of data into groups for further analysis. However, a clear understanding of the data and its features is mandatory to classify the data.

Data of the target application should be built with the live and historical data.

Logistic regression, regularization, stochastic gradient descent optimization, and linear classifier are used to classify the data.

Run through the data, apply the techniques and classify the data.

The following GitHub repositories offer tremendous scope for several projects on Machine learning. Using Libraries, packages, and algorithms provided by GitHub, several projects have been developed.

TensorFlow: An open-source library with data flow graphs that facilitates number crunching. Each node in the graph indicates a mathematical operation, and the edges indicate the tensors (data arrays) flowing. This can be deployed flexibly in any computing environment without any code changes.

NLTK: Natural language Tool kit using natural language processing.

Scikit-Learn: A machine learning module developed in Python deployed over SciPy.

Keras: Python API in neural networks and it is supported in Theno / TensorFlow. They are used more in R & D areas to achieve faster results.

Pattern: Data Mining, NLP, ML tool developed in Python.

Prediction IO: REST APIs are used in this open-source ML framework for the deployment of Algorithms and predictive results querying.

MXNet: An efficient and flexible deep learning tool.

GitHub gives an open door to engineers to make ML projects by giving them a stage to create, reference/information to hone it, and host it to improve perceivability.

In this article, we will know about what is GitHub Machine Learning Projects and their repositories.

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Trending Blogposts on machine learning

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