The need for OpenSource machine learning projects that are faster and precise while handling heavy intricate calculations continues to rise at every turn of the second.

So with this in mind, we have chosen a selection of machine learning projects Ideas to look out for this year.

The goal at the end is to give you avenues of machine learning projects for beginners to help you in your quest to tap into the awesome power of machine learning technologies in your day to day tasks.

1. Shogun

Shogun came to be when the need for bioinformatics became a reality. Written in C++ Shogun is an open-source machine learning library that packs various algorithms and data structures to tackle machine learning related problems. It also has support for pre-calculated kernels.

As a machine learning library, Shogun supports plenty of algorithms such as support vector machines, K-Nearest Neighbors, Dimensionality reduction, and Hidden Markov Models algorithms. Shogun does all this while also acting as an interface for programming languages like Octave, Python, Lua, Java, Ruby, R, C#, and Matlab.

Why Shogun:

Shogun has a great processing capability being able to handle great datasets of 10 million samples.

Contribution to the Shogun package is immense with a vibrant community of users actively contributing to and using the framework for research and education.

Shogun supports a multitude of programming languages and platforms (MacOS, Linux/Unix, and Windows) while seamlessly working together with scientific computing environments.

A state of the art structure and performance packed within Shogun. All this courtesy of software architecture that is unique and unique plus rapid prototyping of data pipelines achieved by a simplified combination of algorithm classes, data representations, and general function tools

2. Theano

Another open-source library that has been in the industry with a long background and reputation worth mentioning is Theano. Theano gives you that efficient swift library for definition, optimization, and evaluation of mathematical expressions.

Theano is built for deep learning with the capability to take on those tasks that demand great neural network algorithms.

Packed with the power of NumPy, Theano neatly packed together all your structures to give clean and efficient code. This gives you code that is compiled to run without hitches on any architecture be it GPU or CPU.

Why Theano:

Theano improves efficiency in symbolic differentiation.

Theano has self-verification capabilities.

An in-depth and extensive unit testing

With Theano you experience maximized performance of hardware through optimizations in code that are quite intelligent.

3. Apache Mahout

The machine learning framework that is Apache Mahout is all about classification, batch collaborative filtering, and clustering. This linear algebra framework works at churning out and executing machine learning algorithms that are scalable.

Apache Mahouts’ implementation is done with the use of the paradigm MapReduce on top of Apache Hadoop.

Mahout supports implementations such as Distributed Naive Bayes classification and Complementary Naive Bayes while also including vector and matrix libraries. Experience evolutionary programming through the distributed fitness function capabilities found this great framework.

Various tech powerhouses have shown their faith in what Mahout can do. Twitter, Facebook, Yahoo, Foursquare and LinkedIn have all joined the Mahout bandwagon and are using the framework in-house.

Why Apache Mahout:

It is the most suitable alternative that mathematicians, statisticians, and data scientists can use for their algorithms.

Mathematically Expressive Scala DSL

Support for Multiple Distributed Backends (including Apache Spark)

Modular Native Solvers for CPU/GPU/CUDA Acceleration

4. Scikit-learn

All machine learning enthusiasts are in agreement that Scikit-learn is one renowned python machine learning library. What began as a Google Summer of code is now a fast, efficient and reliable Cython utilizing machine learning library.

You will get an ample algorithm selection that you can utilize for all the model selection, classification, clustering, regression, and preprocessing you may need.

Why scikit-learn:

It seamlessly integrates and works with other libraries like NumPy and SciPy.

It is quite user-friendly.

Plenty of examples and tutorials available

5. Google ML Kit for Mobile

Google ML Kit for Mobile is targeted at developers in the mobile industry. With it, the ability to create apps that are personalized and engaging becomes a reality. The framework makes for aspects like face detection, text recognition, image labeling, landmark detection, and bar code scanning when making mobile apps.

It has been announced that developers will soon be able to receive text snippet feedback from context-based use.

Why It Google ML Kit for Mobile:

Features that were only available to Google on mobile are now accessible for mobile app developers

6. Gym and BaseLines by OpenAI

The people at OpenAI came together and put out projects that promote and develop artificial intelligence that can be labeled as safe. Having developed various toolkits over time, two have become relatively popular. Due to their ability to develop, compare, and implement reinforcement learning Algorithms Gym and BaseLines have become popular with developers.

The team of over 60 dedicated round the clock researchers led by tech billionaire Elon Musk actively churns interesting documentation on the capabilities of AI alongside other open-source software tools.

Why Gym and BaseLines:

They have immense support for teaching agents covering a wide range such as game playing, walking and much more.

7. Apple’s Core ML

Apple has put together Core ML which brings to the industry integration of trained machine learning models in a simple way into apps found in iOS, macOS, and tvOS. Through an easy set of steps, the ability and access to the model are in your hands. Take the model file and place it in your project, Xcode then automatically creates either a Swift wrapper or Objective-C class and you are well on your way.

Core ML offers comprehensive support of GameplayKit, image classification, sentence classification, word tagging, Natural language processing, object tracking, and barcode detection for the purpose of learned decision tree evaluation.

Core ML has the ability to leverage both GPUs and CPUs to give you maximum performance since it is built upon Metal and Accelerate technologies.

The in-device running of the models guarantees privacy which also maintains the functionality of the application even when you are not connected to the internet.

Why Apple’s Core ML:

It gives amazing and fast performance

easily integrates machine learning models

enables you with just a few lines of code to develop intelligent featured apps

use playgrounds and Create ML in Xcode 10 to create of your own models on Mac.

Optimized for on-device performance

no need for a dedicated server use your Mac to train models from Apple with your custom data

8. Keras

Another open-source machine learning library comes in the form of Keras. Keras has been in the scenes since 2015 but has made headway and stands as one of the best projects to look out for 2019.

Keras main focus has revolved around user-friendliness, extensibility, and modularity. Another thing worth noting is Google’s support for Keras in the TensorFlow core library since 2017.

Keras has a structure of layer that are predefined and purposefully organized in categories labeled core, pooling, normalization, locally connected, convolutional, embedding, noise, and advanced activations.

These layers have specified tasks to perform usually involving passing of compute-intensive operations to backend elements like TensorFlow or Microsoft Cognitive Toolkit.

Why Keras:

user-friendly, and extensible.

supports recurrent and convolutional networks

only a single line of code for one layer is necessary for sequential models

9. Apache MXNet

MXNnet was taken up by Amazon for AWS as the main deep learning framework. The builders of MXNet intended it to have it spread out on a dynamic cloud setup through a distributed parameter server. The linear scaling that MXNet can do cuts across numerous servers and GPUs.

MXNet has provisions for symbolic and imperative styles of programming while supporting a wide range of API languages such as JavaScript, C++, Julia, Python, Perl, Scala, and Go.

As an open-source, machine learning framework MXNet enjoys support from some tech giants and research establishments like Intel, Microsoft, Baidu, and MIT.

Why Apache MXNet:

provides for efficient scalability on multiple GPUs across different hosts.

provision for multiple language APIs around

is supported by top tech and research institutions

10. Microsoft Cognitive Toolkit (CNTK)

This framework breaks the mold as an open-source project from Microsoft. Microsoft Cognitive Toolkit defines neural networks in the form of computational steps with the use of directed graphs. Its developers have used high-level production readers and algorithms to operate efficiently with extensive datasets.

The Microsoft Cognitive Toolkit now allows developers the ability to combine and realize recognized model types which include feed-forward deep neural networks, convolutional neural networks, and recurrent networks.

Why Microsoft Cognitive Toolkit:

Handles several neural network tasks faster, and has an extensive set of APIs.

Highly optimized, built-in components

Efficient resource usage

Easily express your own networks

Training and hosting with Azure

11. PyTorch

This machine learning library based on Torch and Caffe2 is built for Python with its primary development done by Facebook. PyTorch is widely applied in natural language processing applications.

PyTorch features Deep Neural Networks and Tensor computation with elevated GPU acceleration that is intended for maximized flexibility and accuracy.

PyTorch’s development is intended for integration into Python to enable it to work with mainstream libraries and packages like Cython and Numba.

Why PyTorch:

Most suitable to deliver projects that require deployment in the least amount of time

12. TensorFlow

The team at Google Brain has probably put together the best machine learning library available. The key plus point is Tensor Flow is a project designed for massive-scale machine learning and complex computation. On the front end, TensorFlow uses Python to give a useful front end API that creates apps within the framework. For fast computations, C++ is used to execute all matrix multiplications.

The capabilities of TensorFlow cover image recognition, word embedding, recurrent neural networks, natural language processing, partial differential equation, and handwritten digit classification all achieved by running and training deep neural networks.

Why TensorFlow:

Experience abstraction capabilities as everything else is taken care of behind the scenes

backing of Google

an interactive overview of how graphs run via TensorBoard visualization suite

Conclusion

As a developer having and using the right machine learning tools and machine learning projects ideas will help you in the quest for putting together an algorithm that will tap into the strengths and capabilities of the machine learning project of your choice.