Introduction to Scikit Learn

Scikit-learn comes under the Machine Learning library for Python. It is developed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. It can be used for:

• Data Preprocessing
• Dimensionality Reduction
• Supervised Learning
• Unsupervised Learning
• Model Selection

Scikit-learn is not difficult to learn, but it does require some prior knowledge of machine learning concepts. The scikit-learn library is well-documented, and there are many resources available to help users get started which makes it easier for beginners to start learning it. You can start with the basics of Scikit-learn by enrolling in Great Learning’s free Scikit-learn course.

The reply to this query depends on your specific needs and goals. If you want to learn Machine Learning to build models and perform predictions, then you should learn Scikit-learn. If you desire to learn Machine Learning in order to be able to build Deep Learning models, then you should learn TensorFlow. If you are more interested in Scikit-learn, enroll in Great Learning’s free Scikit-learn course and make your basics stronger.

Scikit-learn’s generality is beneficial for comparing various Machine Learning models against one another, and TensorFlow works on a more advanced level. Both of these are third-party Machine Learning models. It is best if you invest in learning these two in-demand skills. You can learn Scikit-learn from scratch by enrolling in Great Learning’s free Scikit-learn course and gain a free certificate on completing the course.

Yes, you will get a certificate of completion for scikit-learn after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.