5 Online Machine Learning Problem Solving Platforms
2. Google Colab.
Practicing something is the greatest way to learn it. To learn machine learning, there are a variety of theories and lessons available both online and offline. However, one cannot genuinely learn until and until they receive some hands-on instruction in order to learn how to solve difficulties.
We present five online venues where a machine learning enthusiast can practice computational applications in this article.
MachineHack is an online platform for Machine Learning Hackathons hosted by Analytics India Magazine, where participants may test and practice their machine learning capabilities. A newbie can use this platform to learn and practice using prominent machine learning methods including Linear Regression, Multiple Linear Regression, Support Vector Regression, Extreme Gradient Boosting Classification, Naive Bayes, K-Nearest Neighbours, and others using datasets provided by the site. The best part about this platform is that it allows you to practice as many times as you like with no time constraint.
CloudXLab is a cloud platform that offers online video courses, auto-assessment examinations, BootML, a UI-based machine learning model code generator, and 24 hour lab access with the Jupyter environment. Paid online courses such as Big data with Hadoop and Spark, Machine Learning Specialisation, Python for Data Science, Deep learning, and others are available on this site.
There are also free tutorials on Linux fundamentals, Python foundations, NumPy for machine learning, and much more. You have the option of enrolling in both the course and the lab, or only the lab. There are different lab enrollment packages available, ranging from one to six months.
3. Google Colab
Google Colaboratory is a Google Cloud Platform-based platform built on top of the Jupyter Notebook environment (GCP). This platform offers a GPU that is free to use and supports Python 2 and 3. Colab may be used to improve machine learning coding skills as well as learn how to construct deep learning applications. Popular deep learning libraries such as Keras, TensorFlow, OpenCV, and others can also be learned. You can develop and run code, store and share your studies, and access powerful computational resources all from your browser using Colaboratory.
Google’s Kaggle is a Jupyter Notebooks environment that requires minimal setup and may be customized. This platform is quite similar to Google Colab in that both provide free GPUs as well as a big community of publicly available data and code. This site is one of the greatest locations to practice data science computational challenges, with over 19,000 public datasets and 200,000 public notebooks. This cloud computing platform supports Python 3 and R and allows for reproducible and collaborative research, as well as the exploration and execution of machine learning algorithms.
OpenML is an open, collaborative, and automated machine learning environment with capabilities like finding and adding data to analyze, downloading and creating computational tasks, finding and adding data analysis processes, and much more. This cross-platform programming environment for sharing and organizing data, machine learning algorithms, and experiments is an open scientific platform for machine learning.
It’s intended to build a seamless networked ecosystem that can be readily integrated into current programming or environments. It offers students, citizen scientists, and practitioners a helpful learning and working environment. OpenML is a great place to learn about and reuse the best solutions for specific analytical challenges, as well as engage with other scientists.
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