If you're a beginner looking to step into the world of Data Science and Machine Learning, Scikit-Learn is a must-learn tool. The Step by Step guide in mastering Scikit-Learn (2021) course is designed to teach beginners to employ Scikit-Learn effectively.
The Course Content
The course demonstrates how to execute data analysis and model creation using Scikit-Learn. It covers the complete workflow of Scikit-Learn, from data gathering to modelling. The presenter explains how Pandas, Numpy, Matplotlib, and Seaborn functions work with Scikit-Learn. Furthermore, the course delves into creating both classification and regression models for supervised learning.
The Step by step guide in mastering Scikit-Learn includes various datasets that cover multiple aspects of modelling. The presenter also covers estimators selection based on data. In addition, the course explains how to alter hyperparameters and model parameters to enhance the model's accuracy.
Users will understand the following concepts in detail:
- Getting data ready
- Choosing estimators
- Fitting the data
- Predicting values
- Evaluation of results
- Improving the results of the model
- Saving the model
Course Quality
Although the course only has a small number of reviews, the overall rating is 3.15. This rating may be due to the course's difficulty level, which may not be suitable for complete beginners.
The course content is excellent, covering all of the essential Scikit-Learn concepts. Furthermore, the presenter uses various datasets to cover different modelling aspects comprehensively.
The course is perfect for students who already have a basic understanding of data science and are familiar with both Python and Scikit-Learn.
Course objectives and outcomes
Within the course, beginners gain valuable hands-on experience in using Scikit-Learn for supervised learning with the aid of the provided datasets. No prior experience is necessary to get started on the course.
Each lesson demonstrates in detail how data is collected, preprocessed, and used to create a Scikit-Learn model. Students will learn how to choose the appropriate estimators for the data and predict values from the final model.
Furthermore, the course provides a comprehensive understanding of how to evaluate the outcomes and enhance the model's accuracy by tweaking the parameters and hyperparameters. The 'save model' section allows students to save and reutilize the models whenever required.
Why take the course?
The Step by step guide in mastering Scikit-Learn (2021) course is tailored explicitly for beginners who want to gain hands-on experience in implementing Scikit-Learn in real-life data science and machine learning situations.
With the ever-increasing demand for skilled Data Scientists and Machine Learning specialists, Scikit-Learn expertise adds more value to your resume and enhances your employment prospects.
Furthermore, this course is an excellent stepping stone for learners preparing to dive deeper into the areas of Data Science and Machine Learning.
The Verdict
The Step by Step guide in mastering Scikit-Learn (2021) is an excellent course for beginners who are looking to gain hands-on experience working with Scikit-Learn. The course covers all essential topics, including data preparation to model creation, in-depth estimator selection, and parameter adjustments for model enhancement. Although best for students who already have some experience in data science and Scikit-Learn, beginners with a solid understanding of Python can also benefit from the course.
Acquiring a strong grip on Scikit-Learn increases your requirements to become a skilled Data Scientist and Machine Learning specialist. Taking this course will be a step in the right direction for anyone interested in a Data Science or Machine Learning profession.
