We are in that phase of the Industrial Revolution where machine learning-driven algorithms are set to take over a major part of our lives, socially and professionally. The age demands data scientists to take control of their data warehouse and computing skills, and deliver on the promises of automation and artificial intelligence. If you are still pondering over which projects you may get after doing a data science project training, here is a quick read for you.
The article finds seven absolutely no-nonsense Data Science Deep Learning projects that not just pay well but also make a forceful entry into the most innovative stage of the applied computational programming journeys.
Deep Learning Projects
Loan Prediction Data Set: Very Basic Data
The Data Science Deep Learning project that rakes in the highest number of entries belongs to the banking and financial sector. The loan prediction data set solves the problem pertaining to whether a loan amount would be approved or not, and if approved, what are the chanced of it getting fully paid back to the loaner. The Data Science Deep Learning project, if implemented successfully, could solve 90% of the loan-related problems in the coming years.
Marketing And Sales Intelligence
With the maturity of data management, we can expect Data Science Deep Learning algorithms used to determine the chances of every target account in the sales funnel getting converted to solve all Marketing and Sales intelligence-related problems. Predictive analytics is the most popular segment of this Data Science Deep Learning project.
Earthquake Readiness Scale
Scientists have more or less zeroed onto the tectonic activities on Earth. However, the intensity index is yet to be devised for earthquakes of various ranges. Data Science Deep Learning projects involved in earthquake readiness scale could help define the nature of damage expected for areas that are heavily populated, especially in the urban category. For instance, if Delhi were to be an epicenter of an earthquake in 2019, the Data Science Deep Learning algorithm could accurately measure the extent of damage to lives and property. It could also define the number of years that it could take to repopulate the same area as it was during the earthquake.
Military Tactics with Drone and Submerged Vehicles
Data Science Deep Learning used in the military is tactically different from the ones used in the consumer economy. For the military, algorithms are elaborate and focused at dealing with cryptic messages. From flying drones to submerged autonomous vehicles and modules, data scientists study the highest likelihood of the enemy camp attacking the devices and inflicting a permanent damage to the military camp. Still under wraps, the Data Science Deep Learning for military activities employ the most coveted data scientists in the industry with seasoned experience in Python and Hadoop architecture that is combined with in-house computing programs.
Global Warming, Fighting Against the Extinction of Animals and Marine Biology
Well, scientists and environmentalists now have a clear and unprecedented access to data that shows how quickly Earth is zoning into a carbon ball, bereaved of its ozone layer, forest cover and marine ecosystem. Data Science Deep Learning teams involved in environmental work specifically deal with projects related to the conservation of animals and plants, and marine ecosystem. From saving tigers, lions, and elephants on the land, to blue whale and walrus in the ocean, there is a specific Data Science Deep Learning effort happening to conserve the lives of our planet cohabitants.
Aliens, Comets and Cosmic Travel, and Answer to Everything Else
What are the chances of us ever meeting an alien? Would we perish in the comet strike? Will Sun erupt to consume the Earth? When will we travel to the outer space and will we survive the journey?
Astrophysics Data Science Deep Learning projects are fascinating stories, backed by data gathered from the space travels by unmanned space missions and telescopic observations. One day, we will have an answer to everything we ask — Data Science Deep Learning can answer that “EVERYTHING”!
Breast Cancer Detection
Using a labeled dataset of mammograms, deep learning models are used to detect breast cancer. Numerous open datasets are available on Kaggle, providing images obtained from mammography, which you can download for use as your training dataset.
To train and build your deep learning model, options include creating a recurrent neural network (RNN) or a convolutional neural network (CNN) from scratch. Alternatively, you can utilize pre-trained models such as ResNet, VGG16, or VGG19, which are conveniently available as functions in the Keras library.
Recommender systems are widely used applications of machine learning, with companies like Netflix, Spotify, LinkedIn, and Amazon utilizing them to offer relevant content to their users. A few years back, Netflix organized a global contest, providing an open movie dataset to the public.
Participants worldwide competed to develop a user rating prediction model using the provided data, with the winning team receiving a cash prize of 1 million dollars. Although the contest is no longer active, the dataset remains accessible to the public on Kaggle. You can create an artificial neural network, train it on Netflix’s open dataset, and generate user rating predictions if interested.
Deep Learning Projects for Beginners
Object Detection System
While an object detection system may seem similar to a facial recognition system, they are distinct technologies. Once you create an object detection system, it will detect and locate various objects in an image, not just faces. This project holds significant business value, especially for security companies, as they utilize object detection to identify potential threats. Tesla’s autopilot also relies on object detection to recognize objects on the road and avoid collisions while driving.
To construct an object detection system, you can use several pre-trained models like Resnet50, Yolo, and SSD. Many of these libraries are conveniently integrated into Keras and TensorFlow, making it easy for you to commence your work.
Digit Recognition System
In this deep-learning project, your goal will be to construct a model capable of identifying handwritten digits. The widely used digit classification dataset, containing 70,000 images of handwritten digits, will serve as the foundation for training and testing your model.
To develop this digit recognition system, you have two options: either opt for a simple feed-forward neural network or utilize a convolutional neural network. It’s worth noting that the digit recognition project has garnered popularity among beginners in the deep learning field, and thus, its inclusion on your resume might not make it stand out.
Nonetheless, this project presents an excellent starting point for beginners in the industry, offering valuable learning experiences that lay the groundwork for tackling more intricate and advanced deep-learning projects in the future.
Storms and tornadoes can raid any region any time of the year. Data Science Deep Learning projects involve a lot of applied AI to make decisions based on predictive analytics matched with historical data. One of the most popular datasets used in this project belongs to IBM Watson’s weather forecasting module. IBM’s Deep Thunder provides accurate weather forecasting, helping local authorities to prepare against an impending catastrophe.
Data Science Deep Learning for weather forecasting can accurately measure the extent of rainfall, flooding intensity, mapping of areas most likely to witness maximum damage and the likelihood of authorities/rescue missions failing or succeeding in their efforts based on their current readiness.