Top Machine Learning Tools

Table of Contents

Here’s a list of the best Machine Learning tools for small businesses to enterprises that enables systems to analyze data & adapt to improve the accuracy of predicted outcomes.

Introduction

Machine learning is a type of artificial intelligence (AI) using data to predict the outcomes of specific situations. Machine learning technology is implemented across several business sectors to coordinate systems and processes based on predicted outcomes.

If you are looking to improve your organization’s efficiency, you should consider leveraging machine learning tools. These tools help your team build machine learning models that help to generate meaningful insights. this can lead to smarter business decisions in turn & better outcomes across the organization.

What Is Machine Learning?

The science of training a system to collect data & make improvements to themselves is known as Machine Learning. It’s a never-ending loop of self-learning & self-improvement by a system.

The four types of machine learning algorithms:

1. Supervised learning

2. Semi-Supervised learning

3. Unsupervised learning

4. Reinforcement learning

What Are Machine Learning Tools?

Machine learning tools run several algorithms & tests, and even analyze massive data sets to make the optimum business decisions & predictions with the help of Artificial Intelligence (AI). These permutations & combinations are run again based on the quality of outcomes to further improve the services.

Example Of Machine Learning

Here are some industries that utilize Machine Learning quite extensively:

1. Healthcare & medical diagnosis

2. Face detection in images 

3. To commute predictions 

4. Public safety 

5. Agriculture 

6. Smart assistants 

7. Government industry & policymaking

8. Workplace safety

9. Safeguarding the environment  

10. Cyber security

Top 5 Machine Learning Software Tools

1. Amazon Machine Learning

Amazon Machine Learning is one of the easiest machine learning tools out there. This tool can prove to be a boon to you if you have a team of engineers that are inexperienced comparatively or if you don’t have a lot of time at hand.

Features

  • You can choose to take up several services.
  • You can work with several frameworks like Pytorch, TensorFlow, etc.
  • Services are quite simplified and not much machine learning expertise is required.
  • It’s an open platform.
  • Their services are HIPAA compliant for use by medical setups.

Pros

  • Easy to create machine learning models
  • Inexpensive
  • Secure
  • Quick to deploy & scalable

2. IBM

The main thing about IBM’s machine learning tools is their emphasis on ‘ease’. IBM’s services are very easy to use for engineers without much experience in Machine Learning & quick to deploy. With these tools, you can run multiple models with just one click

Features

  • One-click deployment
  • Open-source
  • Easy deployment, model training & performance tuning

Pros

  • Easily manage your model lifecycle
  • Highly scalable
  • Quick deployment
  • Easy integration
  • Proactive customer service

Cons

  • Limited support for Apple products
  • No global support

3. Weka

Weka was engineered for the Cloud especially to address the challenges of space & speed that most other machine learning platforms pose.

Weka is a one-platform machine learning tool. It can be of great help if your dependency on machine learning models & activities is very high, but has limited resources. Weka has proven to be of great help to data mining.

Features

  • Data can be read in multiple formats
  • Management through Accelerated DataOps

Pros

  • Easy to get started with & use
  • Ample storage
  • Great for data testing & analyzing
  • Data sets are neatly organized
  • Great data visualizations
  • Extremely scalable

Cons

  • Outdated UI
  • Can be slow & laggy sometimes 

4. BigML

BigML offers One framework to rule them all. BigML’s machine learning tools want to keep things super simple, thereby helping to reduce effort & cost. BigML allows you to design your Machine Learning outcomes through supervised or unsupervised machine learning methods.

Due to the elimination of multiple machine learning libraries, you can expect reduced costs. You can even make great business predictions

Features

  • Models are interactive & can be exported easily
  • Actions that are repetitive & time-consuming can be automated
  • The dashboard is a shared workspace
  • Open-source

Pros

  • Records are easy to keep & retrieve
  • Supports multiple languages
  • Data stored in Cloud
  • Great customer support

Cons

  • Inaccessible offline
  • Limited features in the free trial

5. PyTorch

As one of the most versatile & robust machine learning tools, PyTorch is all set to be used by anyone. They have partnered with popular Cloud Platforms like AWS & Google Cloud, making it just like a ready product that one has to unpack.

PyTorch has been designed to give everyone optimum results whether you prefer to work in execute in eager mode or graph.

Features

  • Supports deployment on iOS & Android
  • Has several packages for different purposes
  • C++ front-end

Pros

  • Easy to use & deploy
  • Easy to migrate computation to GPU
  • Team collaboration is possible
  • Works easily with the usual Python libraries
  • It’s free to use

Cons

  • Limited community support

Conclusion

We hope the list of the top 5 machine learning tools has proved to be of some help to you. Each of these tools has its edge over the others. 

Now it’s up to you to delve in-depth into each tool & see what suits your business needs best. Always remember, you don’t have to stick to just one machine learning tool anyway. Go crazy – after all, the sky’s the limit to your business growth!

Need help with your machine learning project? appleute is here to help the best of the best industry experts to build the perfect project for your needs. Contact us today to discuss your requirements.

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