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Scikit-learn vs TensorFlow

TensorFlow is an open-source Machine Learning platform with tools and libraries for building and deploying ML-powered applications. 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. A TensorFlow certification is what you need to climb up the ladder of ML expertise.

The curriculum of a TensorFlow certification course is designed in a way that will help you improve the four fundamental skills. AI strongly drives the present job market. The certification officially validates one’s proficiency in TensorFlow and solving ML and Deep Learning programs. Besides, there is scikit-learn, a robust ML library that eases coding and applying ML algorithms in Python. But how are TensorFlow and scikit-learn different? Let’s take a look.

What is Scikit-learn?

Scikit-learn or sklearn in Python is an open-source library that can be used for Machine Learning in Python. The sklearn library is a complete collection of tools that are most efficient for statistical modeling and ML. Some of the sklearn tools include classification, regression, clustering, and dimensionality reduction. Fundamentally written in Python, the scikit-learn library is built upon NumPy, SciPy, and Matplotlib. The library utilizes an integrated and steady Python interface to implement different Machine Learning, pre-processing, cross-validation, and visualization algorithms. Developed in 2007 by David Cournapeau, the latest version of the scikit-learn was released in May 2020.

The unique features of the scikit-learn toolkit that simplify ML include:

  • Feature extraction, which allows the extraction of features from images and text.
  • Cross-validation, a feature that will enable you to check the validity and accuracy of the supervised models on unseen data.
  • Dimensionality reduction, with which the number of data attributes, can be reduced for subsequent summarization, visualization, and feature selection.
  • Supervised learning algorithms include generalized linear models, Support Vector Machines, Decision Trees, and Bayesian methods.
  • ‘Unsupervised learning algorithms’ is a collection that includes cluster analysis, factoring, unsupervised neural networks, and principal component analysis.
  • Clustering, a feature that allows grouping of the unlabeled data.
  • Ensemble methods can be used to combine the predictions of multiple supervised models.

What is TensorFlow?

TensorFlow simplifies your task of creating ML models for the web, mobile, desktop, and cloud. So before you sign-up for the TensorFlow certification, know what TensorFlow is. 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. 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. 

So, irrespective of the language that you are using, TensorFlow has you sorted. In a nutshell, here is what TensorFlow can do:

  • Simple model building: Get an easy start to TensorFlow and ML with Keras, a high-level API used for building and training models. Eager execution allows intuitive debugging and rapid iteration for more flexibility. 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.
  • Powerful ML production: Irrespective of the platform or language you use, TensorFlow facilitates easy training and model deployment. There is TensorFlow Extended (TFX) for a full production Machine Learning pipeline. TensorFlow Lite allows running of the interface on embedded and mobile devices like iOS, Android, Raspberry Pi, and Edge TPU. TensorFlow.js can be used for training and deploying models in JavaScript environments.
  • 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.

Scikit-learn vs TensorFlow

Scikit-learn is a toolkit of unsupervised and supervised learning algorithms for Python programmers who wish to bring Machine Learning in the production system. On the other hand, TensorFlow is a framework that allows users to design, build, and train neural networks, a significant component of Deep Learning. Consequently, scikit-learn differs from TensorFlow in several aspects. Some of the differences are highlighted below:

  • Scikit-learn is a traditional ML library that lets users process the data manually. In contrast, TensorFlow represents a Deep Learning library that can automatically extract useful features from the data. In TensorFlow, the data is refined by the Machine Learning model itself.
  • Scikit-learn has highly abstract models. All of its classifiers and converters have a fixed format. Even though the abstraction restricts the user’s freedom, it increases the model’s efficiency. On the other hand, TensorFlow offers a high degree of freedom. It can be used to accomplish what traditional Machine Learning does, but the algorithms have to be implemented by the user.
  • Scikit-learn is ideal for small or medium-sized, practical ML projects that can be completed on the CPU and has minimal hardware requirements. Such projects usually involve a small amount of data and require the user to process the data and choose the relevant model manually. TensorFlow is used for projects that involve massive amounts of data, demand higher accuracy, and GPU-accelerated operations. These projects’ data processing requirements are low and involve Deep Learning.
  • The scikit-learn library is mainly used for data modeling through a consistent Python interface. Depending on the project goals, one can choose scikit-learn, or Machine Learning with R. Scikit aims to maximize accuracy but has no use in Deep Learning. TensorFlow’s applications include – understanding, perception, classification, prediction, discovering, and creation. Some of its practical use cases are voice and image recognition, video detection, and text translation. However, TensorFlow requires a solid understanding of the concepts of Machine Learning and mathematics.

Conclusion

The popularity of Machine Learning language calls for efficient and robust tools. Scikit-learn, with its versatility and efficiency, serves the needs of academia and the industry to solve real-world Machine Learning problems. On the other hand, TensorFlow takes traditional Machine Learning a step forward to design, build, and deploy Deep Learning models. Moreover, a TensorFlow certification will validate your fundamental knowledge of Machine Learning and its integration into applications and tools. All in all, both scikit-learn and TensorFlow are worth your attention if you want to kick start your career in the world of AI and ML.

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