Today we introduce 10 open source machine learning projects suitable for AI programmers to start with. To start contributing to an open source project, there are a few prerequisites:

1. Learn a programming language: Since you need to write code to participate in open source contributions, you’ll need to learn any programming language. Learning another language at a later stage is easy, depending on the needs of the project.

2. Familiarize yourself with version control systems: These software tools help keep all changes in one place so that they can be invoked at a later stage if needed. Basically, they keep track of every change made over time in the source code. Some popular version control systems are Git, Mercurial, CVS, and so on. Git is the most popular and widely used in the industry.

1. Caliban

This is the machine learning project of the tech giant Google. It is used to develop machine learning research workflows and notebooks in isolated and reproducible computing environments. It solves a big problem. When developers are building data science projects, it is often difficult to build a test environment that can demonstrate the project in real life. Caliban is therefore a potential solution to this problem.

Caliban can easily develop any ML model locally, run the code on the machine, and then try the exact same code in the cloud to execute on a large machine. As a result, Dockerized research workflows are simplified both locally and in the cloud.

2. Kornia

Kornia is PyTorch’s computer vision library. It is used to solve some general computer vision problems. Kornia is built on top of PyTorch and relies on its efficiency and CPU power to compute complex functions.

Kornia is a set of libraries for training neural network models and performing image transformation, image filtering, edge detection, pole-orientation geometry, depth estimation, and more.

3. Analytics Zoo

Analytics Zoo is a unified data Analytics and ARTIFICIAL intelligence platform that integrates TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into one integration pipeline. This can effectively scale from laptops to large clusters to handle the production of big data. The project is maintained by Intel-Analytics.

Analytics Zoo helps AI solutions in the following ways:

  • Help to easily prototype AI models.
  • Scaling is effectively managed.
  • Helps add automated processes to your ML pipeline, such as feature engineering, model selection, and so on.

MLJAR Human automated machine learning

Mljar is a platform for creating prototype models and deploying services. To find the best model, Mljar searches for different algorithms and performs hyperparametric tuning. It provides interesting and fast results by running all the computations in the cloud and ultimately creating an integration model. It then builds a report from AutoML training. Isn’t that cool?

Mljar effectively trains models for binary classification, multiclass classification, and regression.

It provides two interfaces:

  • It can run ML models on your Web browser
  • Provide a Python wrapper on the Mljar API.

The report received from the Mljar contains tables containing information about the scores for each model and the time it takes to train each model. Performance is shown as a scatter plot and a boxplot, so it’s easy to visually check which algorithms perform best of all.

5.DeepDetect

DeepDetect is a machine learning API and server written in C++. If you want to use state-of-the-art machine learning algorithms and want to integrate them into existing applications, DeepDetect is for you.

DeepDetect supports a wide variety of tasks, such as classification, segmentation, regression, object detection, and autoencoder. It supports supervised and unsupervised deep learning for images, time series, text, and more types of data. But DeepDetect relies on external machine learning libraries, such as:

  • Deep learning libraries: Tensorflow, Caffe2, Torch.
  • Gradient lift library: XGBoost.
  • T-sne was used for clustering.

6. Dopamine

Spit is an open-source project of the tech giant Google. It is written in Python. It is a research framework of rapid prototyping reinforcement learning algorithm.

The principles of spit are:

  • Easy experiments: Swapping makes it easy for new users to run experiments.
  • It is compact and reliable.
  • It also contributes to reproducibility of results.
  • It is flexible and therefore makes it easy for new users to try out new research ideas.

7. TensorFlow

Tensorflow is one of GitHub’s most well-known, popular, and easy-to-use open source machine learning projects. It is an open source software library for numerical calculations using data flow diagrams. It has a Python interface that is very easy to use, and there are no interfaces that are not needed in other languages to build and execute computations.

TensorFlow provides stable Python and C++ apis. Tensorflow has some amazing use cases, such as:

  • In speech/voice recognition
  • Text library application
  • Image recognition
  • Video detection
  • … There’s more!

Mentioned image recognition and video detection technology, must carry the current very fire AI + video technology in all fields, fused AI detection, intelligent recognition technology to various video application scenarios, such as: security monitoring, face detection in video, traffic statistics, risk behavior (rising, falling, shoving, etc.) to detect recognition, etc. Typical examples are EasyCVR video fusion cloud service, which has AI face recognition, license plate recognition, voice intercom, PTT control, sound and light alarm, surveillance video analysis and data summary capabilities.

8.PredictionIO

It is built on top of a state-of-the-art open source stack. The machine learning server is designed for data scientists to create predictive engines for any ML task. Some of its amazing features are:

  • It helps to quickly build and deploy engines as Web services on customizable production templates.
  • When deployed as a Web service, it can respond to dynamic queries in real time.
  • It supports machine learning and data processing libraries such as OpenNLP and Spark MLLib.
  • It also simplifies data infrastructure management

9.Scikit-learn

It is a library of free software machine learning tools based on Python. It provides a variety of algorithms for classification, regression, clustering algorithms, including random forest, gradient lifting, DBSCAN.

This is built on top of SciPy, which must be pre-installed so that sci-Kit Learn can be used. It also provides the following models:

  • Integration method
  • Feature extraction
  • Parameter tuning
  • Manifold learning
  • Feature selection
  • Dimension reduction

Note: To learn sciKit-learn follow the documentation: scikit-learn.org/stable/

10. Pylearn2

Pylearn2 is the most popular machine learning library for all Python developers. It is based on Theano. While you can write its plug-ins using mathematical expressions, Theano needs to be optimized and stabilized.

It has some great features, such as:

  • Default training algorithm for training the model itself
  • Model estimation criteria
  • Score matching
  • The cross entropy
  • The logarithmic likelihood
  • Data set preprocessing
  • Contrast normalization
  • ZCA whitening
  • Patch extraction (for implementing class convolution algorithms)