Companies all over the world use machine learning to analyze their business data and make important business strategies. This has led to a huge demand for AI professionals, which has encouraged many employees to take machine learning course.
So, what’s all this hype about machine learning? Let’s find out.
Machine learning is the branch of artificial intelligence that deals with enabling computers to make decisions on their own without any human intervention. Ever thought about how Netflix can suggest the shows you might like based on what you watch? Or how Siri or Alexa can play your favourite song or tell you how hot it is outside? It’s all machine-learning magic!. Below you can also find the best Machine Learning Tools.
In machine learning, algorithms are written and fed data. This data can be sourced from your online purchase history, the web shows you watch online, social media posts, comments, likes, and shared pictures. Based on the data, these algorithms will predict the products you might like, shows you might prefer, and other recommendations.
Machine learning algorithms can handle a lot of data. They even learn from previous computations and operations. Thus, without explicit programming, they mimic human decisions.
Machine learning is broadly divided into the following categories. There are a total of 3 types of machine learning and we are going to discuss each of them line by line to better understand Machine Learning Tools.
In supervised learning, the machine learning algorithm is given some data called training data. This data is labelled or has numbers associated with it. Training data may be in the form of input-output pairs. The algorithm learns the mapping of these data pairs and the relation between them. It can then predict outcomes when new inputs are fed.
Here, the algorithm is fed unlabeled data. So it will predict outcomes by finding out data that can be grouped by its similarities or anomalies. It tries to find data patterns that split them into categories. If it doesn’t determine the right output, then it draws inferences from a variety of datasets to find it.
The trial and error method is used in reinforcement learning to detect a data pattern or find a solution to a problem. The last Learning of Machine Learning Tools is Reinforcement.
The machine learning market is growing at a rapid pace as businesses are using it to solve problems, create better customer experiences, analyze data to launch targeted marketing campaigns and predict the rate of investments. It is expected that by 2024, the global machine learning market will be worth more than $20.83 billion.
More than 83% of IT leaders think that machine learning and artificial intelligence are changing customer engagement. It is believed that machine learning will help in building modern logistic technologies. McKinsey predicts that machine learning(Machine Learning Tools) will continue growing the retail sector and will enhance supply chain performance, reduce logistic costs, and detect anomalies.
More than 71% of modern organizations such as Google and Amazon have stated that they are spending more on machine learning than they did two years ago.
Here is a list of the top machine-learning tools–
The first Machine Learning Tool is TensorFlow. This is a JavaScript library that is used for developing machine learning models and training them too. It is open-source, very flexible, and offers high-level APIs such as Keras that have Eager Execution. It can run on several mobile computing platforms, GPUs and CPUs. Using this, you can easily deploy and train the machine learning model in the cloud.
This tool from Google is a platform where various data scientists and developers run machine learning models. This platform is great if your algorithm is sophisticated and handles a lot of training data(Machine Learning Tools). The platform is useful for deep learning, predictive modelling, training, and modelling. You can efficiently train a complicated model. The training and prediction services can be executed together or independently.
Apache Mahout is an open-source linear algebra framework that supports machine learning. It was launched by the Apache Software Foundation and is popular among data scientists, mathematicians, and machine learning engineers. Techniques such as recommendations, classification, and clustering can be implemented.
You can build scalable algorithms using this framework. It has vector and matrix libraries, and can easily run on Apache Hadoop using MapReduce.
Amazon ML is a cloud-based service offered by Amazon where developers of different skill levels can use machine learning technology easily. It offers visualization tools and wizards that assist in creating machine learning models without knowing complicated ML algorithms. After the models are created, the platform lets developers receive predictions for the app using in-built APIs. The AML is a top Machine Learning tool cloud-based.
You do not have to use the prediction generation code for this. Amazon ML can integrate data from a variety of sources such as Amazon S3 and Redshift. It supports machine learning models such as binary classification, regression, and multi-class classification.
This is a . NET-based machine learning framework with audio processing and image processing libraries coded in C#. It has libraries for machine learning applications such as statistical data processing, pattern recognition, classification, clustering, regression, and linear algebra.
It is used in statistics applications, signal processing, and computer vision. The framework consists of more than 35 hypothesis tests such as non-parametric tests like the Kolmogorov-Smirnov test, Sign Test for the Median, and two-way & one-way ANOVA tests.
Machine learning might be the most powerful tool for companies to develop high-class products for their customers and outshine their competitors. If you are an employee working on ML, the tools discussed here and machine learning tools(course) will surely help.
So, what’s all this hype about machine learning? Let’s find out.
What is Machine Learning?
Machine learning is the branch of artificial intelligence that deals with enabling computers to make decisions on their own without any human intervention. Ever thought about how Netflix can suggest the shows you might like based on what you watch? Or how Siri or Alexa can play your favourite song or tell you how hot it is outside? It’s all machine-learning magic!. Below you can also find the best Machine Learning Tools.
In machine learning, algorithms are written and fed data. This data can be sourced from your online purchase history, the web shows you watch online, social media posts, comments, likes, and shared pictures. Based on the data, these algorithms will predict the products you might like, shows you might prefer, and other recommendations.
Machine learning algorithms can handle a lot of data. They even learn from previous computations and operations. Thus, without explicit programming, they mimic human decisions.
Types of Machine Learning
Machine learning is broadly divided into the following categories. There are a total of 3 types of machine learning and we are going to discuss each of them line by line to better understand Machine Learning Tools.
1. Supervised Learning
In supervised learning, the machine learning algorithm is given some data called training data. This data is labelled or has numbers associated with it. Training data may be in the form of input-output pairs. The algorithm learns the mapping of these data pairs and the relation between them. It can then predict outcomes when new inputs are fed.
2. Unsupervised Learning
Here, the algorithm is fed unlabeled data. So it will predict outcomes by finding out data that can be grouped by its similarities or anomalies. It tries to find data patterns that split them into categories. If it doesn’t determine the right output, then it draws inferences from a variety of datasets to find it.
3. Reinforcement Learning
The trial and error method is used in reinforcement learning to detect a data pattern or find a solution to a problem. The last Learning of Machine Learning Tools is Reinforcement.
The Growth of Machine Learning
The machine learning market is growing at a rapid pace as businesses are using it to solve problems, create better customer experiences, analyze data to launch targeted marketing campaigns and predict the rate of investments. It is expected that by 2024, the global machine learning market will be worth more than $20.83 billion.
More than 83% of IT leaders think that machine learning and artificial intelligence are changing customer engagement. It is believed that machine learning will help in building modern logistic technologies. McKinsey predicts that machine learning(Machine Learning Tools) will continue growing the retail sector and will enhance supply chain performance, reduce logistic costs, and detect anomalies.
More than 71% of modern organizations such as Google and Amazon have stated that they are spending more on machine learning than they did two years ago.
Top 5 Machine Learning Tools
Here is a list of the top machine-learning tools–
1. TensorFlow
The first Machine Learning Tool is TensorFlow. This is a JavaScript library that is used for developing machine learning models and training them too. It is open-source, very flexible, and offers high-level APIs such as Keras that have Eager Execution. It can run on several mobile computing platforms, GPUs and CPUs. Using this, you can easily deploy and train the machine learning model in the cloud.
2. Google Cloud ML Engine
This tool from Google is a platform where various data scientists and developers run machine learning models. This platform is great if your algorithm is sophisticated and handles a lot of training data(Machine Learning Tools). The platform is useful for deep learning, predictive modelling, training, and modelling. You can efficiently train a complicated model. The training and prediction services can be executed together or independently.
3. Apache Mahout
Apache Mahout is an open-source linear algebra framework that supports machine learning. It was launched by the Apache Software Foundation and is popular among data scientists, mathematicians, and machine learning engineers. Techniques such as recommendations, classification, and clustering can be implemented.
You can build scalable algorithms using this framework. It has vector and matrix libraries, and can easily run on Apache Hadoop using MapReduce.
4. Amazon Machine Learning
Amazon ML is a cloud-based service offered by Amazon where developers of different skill levels can use machine learning technology easily. It offers visualization tools and wizards that assist in creating machine learning models without knowing complicated ML algorithms. After the models are created, the platform lets developers receive predictions for the app using in-built APIs. The AML is a top Machine Learning tool cloud-based.
You do not have to use the prediction generation code for this. Amazon ML can integrate data from a variety of sources such as Amazon S3 and Redshift. It supports machine learning models such as binary classification, regression, and multi-class classification.
5. Accord. NET
This is a . NET-based machine learning framework with audio processing and image processing libraries coded in C#. It has libraries for machine learning applications such as statistical data processing, pattern recognition, classification, clustering, regression, and linear algebra.
It is used in statistics applications, signal processing, and computer vision. The framework consists of more than 35 hypothesis tests such as non-parametric tests like the Kolmogorov-Smirnov test, Sign Test for the Median, and two-way & one-way ANOVA tests.
Conclusion
Machine learning might be the most powerful tool for companies to develop high-class products for their customers and outshine their competitors. If you are an employee working on ML, the tools discussed here and machine learning tools(course) will surely help.
0 Comments: