Hello everyone and welcome to this interesting article on machine learning tools. So the era of machine learning is here and it's making a lot of progress in the technological field and according to a gardener report machine learning and artificial intelligence is going to create 2.3 million jobs by 2020 and machine learning ecosystem has developed a lot in the past decade. The AI community is so strong open and helpful that there exist a court library and blog for almost everything.

If you want to start a journey in this magical world now is a great time to start. So we are going to discuss some of the machine learning tools and we'll discuss an exhaustive list of libraries and tools to handle most of the machine learning tasks.

So before that let's understand what exactly is machine learning.

What is Machine Learning.

Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.

Machine learning is a concept which allows the machine to learn from examples and experience and active without being explicitly programmed to make this happens we have a lot of machine learning tools available today.

1. Scikit Learn

So the first tool which I'm going to talk about is scikit-learn. It's not exactly a tool it's a library but it's the initial steps which one should follow. So scikit-learn is a free software machine learning library for the Python programming language it is a simple and efficient tool for data mining and data analysis it has built or numpy, Syfy and matplotlib. It provides a range of supervised unsupervised machine learning algorithms in Python like classification, regression, clustering, the dimensionality reduction and much more so this is one of the basic steps or the basic building block of any machine learning project or application out there so you need to know scikit-learn this is one of the most important tools.

2. KNIME

Next, I'm going to talk about is KNIME which is constants information minor. It is a free and open-source data analytics reporting and integration platform which is built for powerful analytics on a GUI based workflow. This means you do not have to know how to code to be able to walk using the KNIME and derive the insights. What you can do is you can walk all the way from gathering the data and creating models to deployment as well as production. It consolidates all the functions of the entire process into a single workflow you can gather and wrangle the data you can model and visualize you can deploy and manage and you can consume and optimize as well so it's an all-in-one package and the most an important aspect is you do not need to know how to code

3. Tensor Flow

So the next library which I'm going to talk about is one of the best library out there for machine learning Which is Tensor flow. It is created by Google brain team and TensorFlow it's an open-source library for numerical computation and large-scale machine learning when it comes to artificial intelligence framework showdown you will find TensorFlow emerging as a clear winner most of the time now what makes it so special so TensorFlow provides an accessible and readable syntax which is essential for making these programming resources easier to use and being a low-level library provides more flexibility and with the new version of 2.0 it is just going to be on the top of any machine learning or deep learning purposes it is one of the best machine learning tools available and it also uses other high-level API's to make things little smoother and the most important thing is it can run on both CPU as well as GPU and it really helps in graphical purposes like if you are dealing with images videos TensorFlow is the way to go

4. WEKA

Now WEKA which is the Waikato environment for knowledge analysis it is an open-source Java software that has a collection of machine learning algorithms for data mining and data exploration tasks. it is one of the most powerful machine-learning tools for understanding and visualizing machine learning algorithms on your local machine it has both a graphical interface and a command-line interface.

Now the only downside to this is that there is not much documentation or online support available but all in all it's a very good software and it's based on Java it also provides predictive modelling and visualization and it's an environment for comparing learning algorithms and the graphical user interface includes data visualization as well.

5. Py Torch

now next we have a library which is one of the biggest rival of TensorFlow which is py torch or torch so py torch is a python-based library built to provide flexibility as a deep learning deployment platform the workflow of py torch is as close as you can get to the Python scientific computing library numpy it 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 py torch 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 now this framework is embedded with ports to ios and android backends.

6. Rapid Miner

rapid miner the next tool which I'm going to talk about is a data science platform for teams that unites data preparation machine learning and predictive model deployment it has a powerful and robust and graphical user interface that enables user to create deliver and maintain predictive analytics with rapid miner uncluttered disorganized and seemingly useless data becomes very valuable as it simplifies data access and lets you structure them in a way that is easy for you and your team to comprehend now few of the features are that it results in visualization a lot through GUI it helps in designing and implementing analytical workflows.

One of the downsides is that the tool is very costly.

7. Google Cloud Auto ML

Now Google is not very far behind apart from TensorFlow we have the Google cloud auto ml now Google cloud auto ml makes the power of machine learning available to you even if you have limited knowledge of machine learning the Google's human labelling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data now how cool is that they have various products for different purposes which makes it a very good machine learning tools.

For example,

We have Auto ml vision which is used for images We have the auto ml video intelligence which is specifically designed for the video we have the auto ml natural language which is used to structure and get the meaning of the text we have the auto ml translation which dynamically detects and translates between different languages and we have the auto ml tables which means the models on your structured data.

8. Azure Machine Learning Studio

Now we have the azure machine learning studio as well now Microsoft Azure machine learning studio is a collaborative drag-and-drop machine learning tool. You can use to build test and deploy predictive analytics solution
on your data. You drag-and-drop paid assets and analysis modules onto an interactive canvas and connecting them together to form an experiment which you run in the machine learning studio and there is no programming required just visually connecting data sets and modules to construct your predictive analytic model and finally, you just have
to publish it as a web service.

9. Accord .NET

Now accord .NET is a .NET machine learning framework combined with audio and image processing libraries which are completely rewritten in C#. Now the tagline being machine learning made in a minute that's an amazing tagline guy and it is a complete framework for building production great computer vision computer audition signal processing and statistics application libraries are made available from the source code and through the executable installer and the NuGet packet manager.

The only drawback is that it supports .NET that it. Only supports the .NET supported languages it provides algorithm for numeric linear algebra, numerical optimization, statistics, artificial neural network, it also provides supports for graph plotting and visual libraries as well and it has more than 38 kernel function it contains more than 35 hypotheses tests including one way and the two way ANOVA test nonparametric tests.

10. COLAB

Now this is rather an interesting product by Google which is the core laboratory it is a free Jupiter notebook environment that requires no setup and runs entirely on the cloud it is a Google research project created to help decimate machine learning education and research that's a good step forward by Google it is by far one of the top machine learning tools especially for data scientists because you don't have to manually install any of the packages and Libraries just import them directly by calling them you can directly save a project on the Google Drive GitHub or any location in various formats as well and it also supports libraries of Py torch, TensorFlow as well as OpenCV these are pre-installed now this is a major step forward in the machine learning education department by Google so hats off to them.

11. NLTK

NLTK is another tool which I'm going to talk about which is the natural language toolkit and it's an extensive library for natural language tasks it just a core to packaging for all your text processing needs from word tokenization to limitation stemming, dependency, parsing, chunking, chinking, removing the stop words and many more text processing is an extremely important part of any NLP task like language modelling neural machine translation or named entity recognition.

Now, these are a few of the tools basically the libraries and the frameworks which are really really necessary if we are going for machine learning or if you're going for deep learning as a matter of fact and these lists of tools are enough for anyone to get started in the machine learning world.

So guys but this we come to the end of this article and I hope you understood all the tools that I talked about and if you guys have any queries regarding this session please feel free to mention it in the comment section below till then thank you and happy learning.

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