Developed by Google, TensorFlow is an open-source machine learning framework widely used for building and training deep learning models. It provides a comprehensive ecosystem of tools and libraries.
Scikit-Learn is a simple and efficient machine learning library for classical machine learning algorithms. It's built on NumPy, SciPy, and Matplotlib, making it easy to use and learn.
Keras is a high-level neural networks API written in Python. It can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. Keras simplifies the process of building and experimenting with deep learning models.
Developed by Facebook, PyTorch is an open-source machine learning library that is widely used for deep learning tasks. It is known for its dynamic computational graph, making it more intuitive for certain types of models.
Jupyter Notebooks provide an interactive computing environment that allows users to create and share documents containing live code, equations, visualizations, and narrative text. They are widely used for ML experimentation and education.
Google Colab is a free, cloud-based version of Jupyter Notebooks. It provides access to GPU and TPU resources, making it convenient for running machine learning experiments without the need for powerful local hardware.
IBM Watson Studio is a cloud-based platform that provides tools for building, training, and deploying machine learning models. It also offers collaborative features for data science teams.
Automated Machine Learning (AutoML) tools like AutoML and TPOT help automate the process of model selection, hyperparameter tuning, and feature engineering, making it easier for beginners.
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It is known for its speed and efficiency, particularly in image classification tasks.
RapidMiner is an integrated data science platform that provides a visual environment for building machine learning models. It supports a wide range of ML algorithms and data preprocessing techniques.