Top 15 Machine Learning Resource & Tools For ML Engineers To Enhance Them Skills

By Kimberly Cook |Email | Mar 1, 2019 | 9009 Views

Machine learning has grown to be one of the hottest job markets in India with tech giants and startups poring billions to this emerging field. Given the slew of opportunities that it has opened up, both fresh IT graduate and experienced enthusiast are reaching out to learn more about coding and various programming languages to set a better foot in the ML field.

In the midst of this buzz, there are numerous non-programmers who don't exactly know how to code and yet want to delve into machine learning and stay abreast of this field.

We list 15 such machine learning tools for those who have a rough hand in programming.

1. Amazon Lex
This service can be used for building conversational interfaces such as chatbots into any application using voice and text. You can easily build, test and deploy your chatbots directly from the service.

How It Process
This service provides advanced deep learning functionalities of automatic speech recognition for the conversion of speech to text, and NLP to recognize the intent of the text, enabling you to build highly engaging user experiences and lifelike conversational experiences.
2. Auto-WEKA
This is a data-mining tool that performs combined algorithm selection and hyper-parameter optimization over the classification and regression algorithms that are being implemented in WEKA.

How It Process
When a dataset is given, this tool explores the hyperparameter settings for several algorithms and recommends the most preferred one to the user that gives good generalization performance.

3. BigML
BigML is a comprehensive machine learning platform that provides a selection of machine learning algorithms to solve the real world problems by applying a single, standardized framework.

How It Process
This platform covers classification, regression, time series forecasting, cluster analysis, anomaly detection, topic modeling, and association discovery to facilitate unlimited predictive applications for various fields like agriculture, aerospace, healthcare, food, etc.

4. Data Robot
This is an automated machine learning platform by Kagglers to build and deploy accurate machine learning models for all levels of enthusiasts within a fraction of time.

How It Process
It enables users to build and deploy highly accurate machine learning models by automatically detecting the best data pre-processing. It can employ encoding, scaling, text mining, etc. When the dataset is very large, it uses distributed algorithms to scale up the dataset.

5. Driverless AI
Driverless Ai is an artificial intelligence platform for automatic machine learning. The aim is to achieve the highest predictive accuracy in a shorter amount of time by end-to-end automation. It runs on commodity hardware and is designed to make use of GPUs, multi-GPU workstations, etc.

How It Process
This platform automates difficult machine learning workflows like feature engineering, model validation, tuning, selection as well as deployment. The model pipelines like feature engineering and models are exported as Python modules and Java standalone scoring artifacts.

6. Datawrapper
This is an open source platform that helps you generate visualizations like interactive graphs, maps, charts from your data within a short time. No design skills or code is required for it.

How It Works
The functionality in Datawrapper is provided by plugins. It works in three simple steps. Firstly, copy your data and paste it to the live-updating charts, then visualize it by customizing and choosing the types of the charts and maps and finally, publish the ready-made chart as an image or pdf.

7. Fusioo
This is a database application where you can build the tools you need. It gives you the freedom to create your own app to track, manage and share information without writing a single code.

How It Process
The steps are really easy. First, you create an app and name it according to your projector whatever you wish. Then, the next step is to create the Field that you need to track and finally a dashboard will be created for your apps. You can customize it by charts, lists, etc.

8. Google Cloud AutoML
Google Cloud AutoML is a suite of machine learning products that train high-quality custom machine learning models with minimum effort by leveraging Google's state-of-the-art transfer learning and Neural Architecture Search technology.

How It Process
It provides simple GUI for the users to train, evaluate, improve and deploy models based on your data. The data can be stored in cloud storage. To generate a prediction on your trained model, just use the existing Vision API by adding a custom model.

9. IBM Watson Studio
This platform provides you tools for a hassle-free work with your own data to build and train models at scale with a faster optimization. It helps to accelerate the machine learning workflows that are required to infuse artificial intelligence into your business or projects.
How It Process
The working process is easy and simple, You just have to go with the flow. First, choose a project type from the options provided, then define your project and store it into the cloud. Then you can customize by choosing several options like connect to a GitHub repository, link to a service, etc. and use it according to your project.

10. Microsoft Azure Machine Learning Studio
This is a browser-based machine platform that has a visual drag-and-drop environment that requires zero coding knowledge. It can be used by anyone regardless of the level of their skills.

How It Process
Firstly, you need to import your dataset from an excel sheet, etc. The data cleaning and other necessary pre-processing steps are performed. The data is split into training and testing sets and the built-in algorithms are applied to train the model and finally, your model will be scored, and you will get the predictions.

11. ML Jar
It is a human-first platform for machine learning that provides a service for prototyping, development and deploying pattern recognition algorithms.

How It Process
It includes three simple steps to build an accurate machine learning model. First, you need to upload the data with a secure connection, then training and tuning are done on many machine learning algorithms and the best match will be selected according to your data. Finally, use the best models for predictions and share your results.

12. Paxata
Paxata is an organization that provides visual guidance, algorithmic intelligence, and smart suggestions, uses spark for enterprise data volumes, automatic governance, etc.

How It Process
The working process is simple here like you can use a wide range of sources to acquire data, performs data exploration using powerful visuals, performs data cleaning using normalization of similar values using natural language processing, makes pivots on data, combining data frames by SmartFusion, etc.

13. Rapid Miner
This is an open sourced tool that helps in prediction modeling.

How It Process
It creates predictive models by using automated machine learning and data science best practices in just four clicks. This tool automatically analyses data to identify common quality problems like missing values. Then the best model for your data will be optimized by using multiple machine learning algorithms. The feature engineering is automated that lets you choose a balanced model and the predictive model is created.

14. Tableau
This has proved to be the most popular business intelligence and visualization tool in the present scenario. You can create graphs, charts, maps, etc. within a short span of time.

How It Process
Various data sources can be connected in this tool and it has multiple options to represent data in different views, creating sets, applying filters, generating trend lines, forecasting, etc. You can deploy data drilling tools and explore various data that are available without any coding knowledge.

15. Trifacta
Trifacta provides free stand-alone software that offers an intuitive GUI for performing data cleaning.

How It Process
This software takes data as input and evaluates a summary with multiple statistics by column and for each column, it recommends some transformations automatically. The data preparation can be done by various options present in the software like discovering, structure, cleaning, enriching, etc.

Source: HOB