The era of machine learning has been here since a decade but especially right now it has become an industry able enough to generate 2.3 million jobs as of 2021.
The machine learning eco-system has developed so much and the AI community is so strong, open and helpful that there exists code, library and a blog for almost everything.
Before we move further let’s discuss machine learning. ML is a type of artificial intelligence that allows software applications to learn from the data and eventually become more accurate in the prediction of outcomes.
Let it be the artificial intelligence helping Google rank random digital marketing or tech blogs, or softwares getting automated, the concept allows the machine to learn from examples and experiences.
In order to make this happen we have a lot of machine learning tools available to us today:
7 Best Machine Learning Tools You Need To Know
1# Scikit Learn
Well, this one is not exactly a tool it’s a library but these are what we believe the initial steps that one should follow. This is a free software machine learning library for the Python programming language.
This is a simple and efficient tool for data mining and data analysis as well. It is built on NumPy, SciPy and Matplotlib. This tool provides a range of supervised and unsupervised machine learning algorithms in Python like classification, regression, clustering, dimensionality reduction and much more.
So this makes it Scikit Learn a basic building block of any machine learning or application development project out there.
Constant information miner is a free and open source data analytics reporting and integration platform which is used widely for powerful analytics on a GUI based work flow. This means you do not have to know how to code to be able to work using the KNIME.
Whit this tool one can work all the way from gathering information and creating models for deployment as well as production. This tool consolidates all the functions of the entire process into a single workflow, one can gather and wrangle the data, one can model and visualize, deploy and manage and lastly consume and optimize as well.
This makes the tool an all-in-one package for Android or iOS app Development Company as the dependence on the knowledge of coding is significantly reduced.
This is one of the best libraries out there for machine learning. This tools was created by the Google Brain team and is an open source library for numerical computations and large-scale machine learning features.
When it comes to the AI framework showdown, one can find TensorFlow emerging as a clear winner of the times. This tool provides an accessible and readable syntax which is essential for making various programming resources easier to use. Furthermore being a low-level library provides more developmental flexibility.
This is tool uses high-level APIs to make things a little smoother, but the most important thing is it can run on both CPU and GPU. This really help in graphical purposes when the user is dealing with images and videos.
Weka which is the Waikato environment for knowledge analysis is an open source Java software that possesses a collection of machine learning algorithms using widely for data mining and data exploration tasks.
It is considered the most powerful machine learning tools for understanding and visualizing machine learning algorithms on your local machine.
This tools has both, a graphical interface and a command line interface but the only downside to this is that there is not much documentation or online support available. But all in all this is a very good software which is based purely on Java.
In addition to this it also provides predictive modeling with visualization and is an environment for comparing learning algorithms. The graphical user interface includes data visualization as well.
This library is one of the biggest rival of TensorFlow and is a completely Python based library. This tools is built to provide flexibility as a deep learning deployment platform.
The workflow in PyTorch is as close as you can get to the Python Scientific Computing Library “NumPy”. This tool is actively used by Facebook for all of its machine learning or deep learning work and the dynamic computation graphs are a major highlight of PyTorch.
The support for CUDA ensures that the code can run on the GPU thereby decreasing the time needed to run the code and increasing the overall performance of the system. This framework is also embedded with ports to iOS and Android back ends.
RapidMiner is a data science platform used by teams that unite data preparation, machine learning and predictive model deployment. It has a powerful and robust graphical user interface that enables the user to create, deliver and maintain predictive analytics.
With this tool uncluttered, disorganized and seemingly useless data becomes very valuable as it simplifies data access and lets you structure them in a way that it becomes easy for the user and their teams to comprehend.
The results are displayed in visualizations and through the use of GUI it enables the designing and implementing analytical workflows. One of the downside is that tools itself is really costly.
7# Google Cloud AutoML
Google is not very far behind; apart from TensorFlow we have the Google Cloud AutoML. This tools makes the power of machine learning available to you even if you have limited knowledge of machine learning.
Google’s Human Labeling Service can out a team of people of work annotating or cleaning the labels to make sure your models are being trained on high quality data. Now, how good is that? They have different products for various purposes which makes this a very helpful machine learning tool.
We have AutoML Vision, which is used for images, we have the AutoML Video Intelligence which is specifically designed for video, we have the AutoML Natural Language which is used to structure and get the meaning of text and we also have the AutoML Translation which can dynamically detect and translate between different languages.
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Author Bio: Sami is a Digital Marketing Strategist at CMOLDS, anin the US. Having worked on multiple online brands throughout his career with an expertise in content marketing, email marketing, lead generation, and conversion rate optimization.