Ali Mohsin

Top 10 Machine Learning demos of 2022 from Data Science Dojo

In this blog, we will have a look at the list of top 10 Machine Learning Demos offered by Data Science Dojo that will provide ease to use ML (Machine Learning) techniques free.

With more people entering Data Science, Machine Learning and Artificial Intelligence are among the top emerging areas of work in the 21st century. Many people are opting for this area for them.

The other perspective to view the situation is to utilize these innovative technologies in business. For this reason, recently Data Science Dojo has revamped its platform called Machine Learning Demos. The primary benefit of using these demos is that a few of them are programmed on Azure APIs while others are trained on different ML models, and we can easily use them free of cost.

Machine learning demos from DSD

DSD offers a lot of training and boot camps Data Science Bootcamps to get started with the field, so these demos are also an add-on to our teaching.

So, if you are interested in exploring the practical applications of this modern tech, this set of free ML demos can help you a lot in many ways. The top ones are listed below go and check them out:

*Top 10 machine learning demos – Data Science Dojo*

1. Cleanse stop words:

This demo uses the Azure services for the backend while according to the user point of view, it has quite easy to use Interface and we can use this demo to make text data cleaner for ML models. Go to Cleanse Stop words demo input your text data and get the cleaned text in just one click.

2. Text entity extractor:

Entity extraction helps to sort the unstructured data and find valuable information from the given text. This demo is based on Azure API. It’s simple UI (User Interface) provides an effortless way to use azure services for entity extraction. Go to Text Entity Extractor demo and just input your text to categorize it based on semantic type.

3. Opinion mining:

Sentiment analysis, also referred to as opinion mining, is one of the key techniques in Natural Language Processing (NLP). The business view of opinion mining is highly appreciable as it leads to extracting sentiments from customers’ feedback. This demo is based on Azure Text API while its UI efficiently separates the praises and complaints from the given text. Try Opinion Mining Demo!

4. American sign language detection:

Systems for recognizing sign language are being developed to make it easier for signers and non-signers to communicate. This demo is built on Python famous package called Mediapipe with some other packages like Tensorflow, Cvzone and Numpy. Go to Sign Language demo, and when the user inputs an alphabet using the right hand in the camera it detects the alphabet.

5. Wikipedia article scrape:

Besides the fact that Wikipedia is free, it is an also open multilingual content online encyclopedia. This demo is based on famous python packages Wikipedia and Worcloud. This demo really helps in research to find the articles. Go to Wikipedia Article Scrape, and give the article name and language code and scrape the article to extract content, linked articles etc.

6. Credit card streamer:

We have a few Data streaming demos; Credit Card Streamer is one from that category. This demo is based on Azure SDK in python, give the endpoint string of Event Hub, and set the stream, it will connect this app to Event Hub and your swipes send to Azure Event Hub. Go to Credit Card Streamer and try.

7. Paraphrasing:

The basic objective of paraphrasing is to translate the original message into your own words to demonstrate that you have understood the paragraph sufficiently to restate it.

This demo is built on Python, and it uses a transformer library with some other famous Python packages like PyTorch, timm, sentence piece, and sentence-splitter. Go to the Paraphrasing demo, it uses natural language processing to create a paraphrasing of your input text.

8. Titanic survival predictor:

This demo is unique from our predictive demos category and is based on Azure API. It will predict that the person would survive the Titanic Disaster based on the given required inputs. The backend is built on Python while the UI gives the message based on chances of survival. Go to the Titanic Survival Predictor demo and try it once (just for curiosity 😊)

9. Question generator:

This demo is built on a Python library transformer. Transformers package contains over 30 pre-trained models and 100 languages, along with eight major architectures for natural language understanding (NLU) and natural language generation (NLG).

In educational purposes, we can use this demo. It saves teachers time and effort to make a quiz related to the given content. Go to Question Generator demo, just give the context of the question and the correct answer then click submit, this demo automatically generates the Question based on given inputs.

10. Bike sharing demand predictor:

The last demo we are going to discuss in this blog is also from the list of predictive demos category. This demo uses Azure API for predicting the demand of bike sharing while the UI allows you to change the inputs dynamically from sliders. Must go and check Bike Sharing Demand Predictor.

Stay updated for interesting ML demos

Recently in 2022, we have revamped our demo site completely. And now we have 29+ demos on our site. We have categorized them into categories for the ease of users so that they can pick the demo based on tasks, these are only a few top ML demos, other than these, we do have many informative and interesting demos on this site.

Once you are familiar with data-driven tasks it is most important to utilize them for improving our businesses, we have received a lot of positive feedback from the customers this year that motivates us to improve and add more advanced demos to our site. I assure you; it is worth it to use, go, and explore:

Machine Learning | Resources

Apache Airflow: Monitor and manage the data pipelines and complex workflows

Data Science Dojo is offering Apache Airflow for FREE on Azure Marketplace packaged with a pre-configured web environment of Airflow with various data analytics features.

Introduction:

In this era of tighter data restrictions, it is more important than ever to understand, analyze, and manage your data throughout its lifecycle. It is harder than ever as data volumes rise, and data pipelines get more complicated. A solution is needed Organizations or Individuals must have a complete, scalable, easy-to-analyze platform to manage and monitor the complex workflows and support several integrations.

What is Apache Airflow?

Apache Airflow, a powerful open-source tool for authoring, scheduling, and monitoring data and computational workflows. It provides a method that makes it easier to manage, schedule, and coordinate complicated data pipelines from several sources.

What is DAG?

A DAG, or Directed Acyclic Graph, in Airflow is a list of all the jobs you wish to execute, arranged to reflect their connections and dependencies. A Python script that expresses the DAG’s structure as code defines a DAG. Researchers’ priori ideas about the connections between and among variables in causal structures are encoded using DAGs. It contains directed edges (arrows), linking nodes (variables), and their paths. Hence A workflow is represented as a DAG, which consists of discrete units of work called Tasks that are ordered considering relationships and data flows.

Apache Airflow Architecture:

This powerful and scalable workflow scheduling software is made up of four key parts:

Scheduler: The scheduler keeps track of all DAGs and the jobs they are connected to. To start, it frequently checks the list of open tasks.
Web server: The user interface for Airflow is the web server (The default port Apache Airflow listens to is 8080). It displays the status of the jobs, gives the user access to the databases, and lets them read log files from other remote file stores like Microsoft Azure blobs.
Database: To make sure the schedule retains metadata information, the state of the DAGs and the tasks they are connected to, are saved in the database. The scheduler scans each DAG and records essential data, including schedule intervals, run-by-run statistics, and task instances.
Executors: There are various kinds of executors for different use cases. Few examples of Executors are SequentialExecutor, LocalExecutor, CeleryExecutor, and KubernetesExecutor

(With SequentialExecutor, just one task may be carried out at once. No parallel processing is possible. It is useful when testing or debugging. LocalExecutor supports hyperthreading and parallelism. It is excellent for using Airflow on a single node or a local workstation. CeleryExecutor is usually used for managing a distributed Airflow cluster. While using the Kubernetes API, the KubernetesExecutor creates temporary pods for each of the task instances to run in.)

Key features Apache Airflow provides:

Dynamic Pipelines can be constructed by Airflow dynamic, also as it is constructed in the form of code which gives an edge to dynamic behavior.
Apache Airflow has a rich User Interface that helps the user to manage their workflow easily
It gives a separate code view pallet that enables users to view their DAGs code as well.
Allows users to visualize their DAGs in different forms like Gantt chart, Tree, and Graph.
With ready to use operators in airflow, users can work with various cloud platforms like Microsoft Azure, AWS (Amazon Web Services) etc.
Allows role-based user management to maintain Security and Accessibility.

Apache Airflow with Azure services:

Apache Airflow leverages the power of Azure services to make the procedure of monitoring and managing complex workflows intuitively. Also with Azure, Airflow made it a more scalable data warehousing platform. Airflow enables users to work in a scalable environment.

Conclusion:

Other open-source Data Engineering solutions put intense competition on Apache Airflow. But it is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. Users can easily visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status all in a single package.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We therefore know the importance of data and the encapsulated insights. Through this offer, we are confident that you can analyze, visualize, and query your data in a collaborative environment with greater ease.

Install the Apache Airflow offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science!

Click on the button below to head over to the Azure Marketplace and deploy Apache Airflow for FREE by clicking on “Try now.”

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.

Data Engineering

Hands-on deep learning using Python in Cloud

Data Science Dojo has launched  Jupyter Hub for Deep Learning using Python offering to the Azure Marketplace with pre-installed Deep Learning libraries and pre-cloned GitHub repositories of famous Deep Learning books and collections which enables the learner to run the example codes provided.

What is Deep Learning?

Deep learning is a subfield of machine learning and artificial intelligence (AI) that mimics how people gain specific types of knowledge. Deep learning algorithms are incredibly complex and the structure of these algorithms, where each neuron is connected to the other and transmits information, is quite similar to that of the nervous system.

Also, there are different types of neural networks to address specific problems or datasets, for example, Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs).

While in the field of Data Science, which also encompasses statistics and predictive modeling, deep learning contains a key component. This procedure is made quicker and easier by deep learning, which is highly helpful for data scientists who are tasked with gathering, processing, and interpreting vast amounts of data.

Deep Learning using Python

Python, a high-level programming language that was created in 1991 and has seen a rise in popularity, is compatible with deep learning, which has contributed to its development. While several languages, including C++, Java, and LISP, can be used with deep learning, Python continues to be the preferred option for millions of developers worldwide.

Additionally, data is the essential component in all deep learning algorithms and applications, both as training data and as input. Python is a great tool to employ for managing large volumes of data for training your deep learning system, inputting input, or even making sense of its output because it is primarily used for data management, processing, and forecasting.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

Challenges for individuals

Individuals who want to upgrade their path from Machine Learning to Deep Learning and want to start with it usually lack the resources to gain hands-on experience with Deep Learning. A beginner in Deep Learning also faces compatibility issues while installing libraries.

What we provide

Jupyter Hub for Deep Learning using Python solves all the challenges by providing you with an effortless coding environment in the cloud with pre-installed Deep Learning Python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Moreover, this offer provides the user with repositories of famous authors and books on Deep Learning which contain chapter-wise notebooks with some exercises that serve as a learning resource for a user in gaining hands-on experience with Deep Learning.

The heavy computations required for Deep Learning applications are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

Listed below are the pre-installed Python libraries related to Deep learning and the sources of repositories of Deep Learning books provided by this offer:

Python libraries:

NumPy
Matplotlib
Pandas
Seaborn
TensorFlow
Tflearn
PyTorch
Keras
Scikit Learn
Lasagne
Leather
Theano
D2L
OpenCV

Repositories:

GitHub repository of book Deep Learning with Python 2nd Edition, by author François Chollet.
GitHub repository of book Hands-on Deep Learning Algorithms with Python, by author Sudharsan Ravichandran.
GitHub repository of book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by author Geron Aurelien.
GitHub repository of collection on Deep Learning Models, by author Sebastian Raschka.

Conclusion:

Jupyter Hub for Deep Learning using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through this offer, a user can work on a variety of Deep Learning applications self-driving cars, healthcare, fraud detection, language translations, auto-completion of sentences, photo descriptions, image coloring and captioning, object detection, and localization.

This Jupyter Hub for Deep Learning instance is ideal to learn more about Deep Learning without the need to worry about configurations and computing resources.

The heavy resource requirement to deal with large datasets and perform the extensive model training and analysis for these applications is no longer an issue as heavy computations are now performed on Microsoft Azure which increases processing speed.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data.

We are therefore adding a free Jupyter Notebook Environment dedicated specifically to Deep Learning using Python. Install the Jupyter Hub offer now from the Azure Marketplace, your ideal companion in your journey to learn data science!

Data Science | Programming

Learn Data Visualization with Python in Cloud

Data Science Dojo has launched Jupyter Hub for Data Visualization using Python offering to the Azure Marketplace with pre-installed data visualization libraries and pre-cloned GitHub repositories of famous books, courses, and workshops which enable the learner to run the example codes provided.

What is data visualization?

It is a technique that is utilized in all areas of science and research. We need a mechanism to visualize the data so we can analyze it because the business sector now collects so much information through data analysis. By providing it with a visual context through maps or graphs, it helps us understand what the information means. As a result, it is simpler to see trends, patterns, and outliers within huge data sets because the data is easier for the human mind to understand and pull insights from the data.

Data visualization using Python

It may assist by conveying data in the most effective manner, regardless of the industry or profession you have chosen. It is one of the crucial processes in the business intelligence process, takes the raw data, models it, and then presents the data so that conclusions may be drawn. Data scientists are developing machine learning algorithms in advanced analytics to better combine crucial data into representations that are simpler to comprehend and interpret.

Given its simplicity and ease of use, Python has grown to be one of the most popular languages in the field of data science over the years. Python has several excellent visualization packages with a wide range of functionality for you whether you want to make interactive or fully customized plots.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your visualization skills.

Data visualization using Python — Using Python to visualize Data