AI & Deep Learning with TensorFlow
- 16k Enrolled Learners
- Live Class
The era of Machine Learning is here and it’s making a lot of progress in the Technological field and according to a Gartner Report, Machine Learning and AI is going to create 2.3 million Jobs by 2020 and this massive growth has led to the evolution of various Machine Learning Tools that we will discuss in this article.
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 that too without being explicitly programmed. To make this happen we have a lot of Machine Learning Tools available today. Let’s have a look at some of the most important and popular ones.
It provides a range of Supervised and Unsupervised learning algorithms in Python like Classification, Regression, Clustering, Dimensional reduction.
KNIME (Konstanz Information Miner), is a free and open-source data analytics, reporting, and integration platform built for powerful analytics on a GUI based workflow. This means you do not have to know how to code to be able to work using KNIME and derive insights.
You can work all the way from Gathering Data and Creating Models to Deployment and Production. It consolidates all the functions of the entire process into a single workflow.
Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. When it comes to Artificial Intelligence frameworks showdown, you will find TensorFlow emerging as a clear winner most of the time.
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 v2.0, it’s just going to be on the top for any Machine Learning or Deep Learning purpose. It is one of the best Machine Learning Tools available.
WEKA (Waikato Environment for Knowledge Analysis) 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 Command Line Interface. The only downside to this is that there is not much documentation and online support are available.
Pytorch is a python based library built to provide flexibility as a deep learning development platform. The workflow of PyTorch is as close as you can get to python’s scientific computing library – NumPy. It is actively used by Facebook for all of its Machine Learning or Deep Learning work.
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.
RapidMiner is a data science platform for teams that unites data prep, machine learning, and predictive model deployment. It has a powerful and robust graphical user interface that enables users to create, deliver, and maintain predictive analytics.
With RapidMiner, uncluttered, disorganized, and seemingly useless data becomes very valuable as it simplifies data access and lets you structure them in a way that it is easy for you and your team to comprehend.
Google Cloud AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. Google’s human labeling 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. How cool is that!
They have various products for Different purpose which makes it a very good Machine Learning Tool. Some of them are:
Microsoft Azure Machine Learning Studio is a collaborative, Drag-and-Drop Machine Learning Tool you can use to build, test, and deploy predictive analytics solutions on your data.
You drag-and-drop datasets and analysis modules onto an interactive canvas, connecting them together to form an experiment, which you run in Machine Learning Studio. There is no programming required, just visually connecting datasets and modules to construct your predictive analysis model.
Accord.NET is a .NET machine learning framework combined with Audio and Image processing libraries completely written in C#. The tagline being “Machine Learning Made in a Minute“.
It is a complete framework for building production-grade computer vision, computer audition, signal processing, and statistics applications. Libraries are made available from the source code and also through executable installer & NuGet package manager. The only drawback is that it supports.Net supported languages only.
CoLab (Colaboratory) is a free Jupyter Notebook environment that requires no setup and runs entirely in the cloud. It is a Google research project created to help disseminate machine learning education and research.
It is by far one of the top Machine Learning Tools especially for data scientists because you don’t have to manually install all the packages and libraries, just import them directly by calling them. You can directly save your project on Google Drive, GitHub or any location and in various formats too.
And with this, we come to an end of this article. I hope you have enough options to start building your Machine Learning Program and work on it. Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning, and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.