Scikit-learn is a toolkit of unsupervised and supervised learning algorithms for Python programmers who wish to bring Machine Learning in the production system. TensorFlow also has a storehouse of robust add-on libraries and models that can be experimented with, including TensorFlow Probability, Ragged Tensors, BERT, and Tensor2Tensor.TensorFlow.js can be used for training and deploying models in JavaScript environments. TensorFlow Lite allows running of the interface on embedded and mobile devices like iOS, Android, Raspberry Pi, and Edge TPU. There is TensorFlow Extended (TFX) for a full production Machine Learning pipeline. Powerful ML production: Irrespective of the platform or language you use, TensorFlow facilitates easy training and model deployment.If your interest lies in large Machine Learning training tasks, the Distribution Strategy API allows distributed training on distinct hardware configurations without altering the model definition. Eager execution allows intuitive debugging and rapid iteration for more flexibility. Simple model building: Get an easy start to TensorFlow and ML with Keras, a high-level API used for building and training models.In a nutshell, here is what TensorFlow can do: So, irrespective of the language that you are using, TensorFlow has you sorted. TensorFlow offers many workflows for developing and training models using JavaScript, Python, or Swift, which can be easily deployed in the cloud on-device or the browser. It’s an end-to-end platform that allows easy building and deployment of models to solve challenging real-world problems related to Machine Learning. So before you sign-up for the TensorFlow certification, know what TensorFlow is. TensorFlow simplifies your task of creating ML models for the web, mobile, desktop, and cloud. Ensemble methods can be used to combine the predictions of multiple supervised models.Clustering, a feature that allows grouping of the unlabeled data.‘Unsupervised learning algorithms’ is a collection that includes cluster analysis, factoring, unsupervised neural networks, and principal component analysis.Supervised learning algorithms include generalized linear models, Support Vector Machines, Decision Trees, and Bayesian methods.Dimensionality reduction, with which the number of data attributes, can be reduced for subsequent summarization, visualization, and feature selection.Cross-validation, a feature that will enable you to check the validity and accuracy of the supervised models on unseen data.Feature extraction, which allows the extraction of features from images and text.The unique features of the scikit-learn toolkit that simplify ML include: Developed in 2007 by David Cournapeau, the latest version of the scikit-learn was released in May 2020. The library utilizes an integrated and steady Python interface to implement different Machine Learning, pre-processing, cross-validation, and visualization algorithms. Fundamentally written in Python, the scikit-learn library is built upon NumPy, SciPy, and Matplotlib. Some of the sklearn tools include classification, regression, clustering, and dimensionality reduction. ![]() The sklearn library is a complete collection of tools that are most efficient for statistical modeling and ML. Scikit-learn or sklearn in Python is an open-source library that can be used for Machine Learning in Python. But how are TensorFlow and scikit-learn different? Let’s take a look. Besides, there is scikit-learn, a robust ML library that eases coding and applying ML algorithms in Python. The certification officially validates one’s proficiency in TensorFlow and solving ML and Deep Learning programs. AI strongly drives the present job market. The curriculum of a TensorFlow certification course is designed in a way that will help you improve the four fundamental skills. A TensorFlow certification is what you need to climb up the ladder of ML expertise. But if you want to become an ML expert, there are four core areas that you need to learn – coding skills, math and statistics, ML theory, and the ability to build ML projects from scratch. TensorFlow is an open-source Machine Learning platform with tools and libraries for building and deploying ML-powered applications.
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