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Hands-on Scikit-learn for Machine Learning
Machine Learning projects with Python’s own Scikit-learn on real-world datasets
This Course Includes
udemy
3.8 (7 reviews )
9 total hours 
english
Online - Self Paced
course
Udemy
About Hands-on Scikit-learn for Machine Learning
Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.
If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn.
By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.
All the code and supporting files for this course are available on Github
About the Author
Farhan Nazar Zaidi has 25 years' experience in software architecture, big data engineering, and hands-on software development in a variety of languages and technologies. He is skilled in architecting and designing networked, distributed software systems and data analytics applications, and in designing enterprise-grade software systems.
Farhan holds an MS in Computer Science from University of Southern California, Los Angeles, USA and a BS in Electrical Engineering from University of Engineering, Lahore, Pakistan. He has worked for several Silicon-Valley companies in the past in the US as a Senior Software Engineer, and also held key positions in the software industry in Pakistan. Farhan works as consultant, solutions developer, and in-person trainer on big data engineering, microservices, advanced analytics, and Machine Learning.
What You Will Learn?
- Tackle real-world problems in Machine Learning through a structured process using Scikit-learn.
- Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn.
- Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA.
- Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically.
- Build a portfolio of tools and techniques that can readily be applied to your own projects.
- Discover the intuition behind contemporary Machine Learning models and algorithms without going into deep mathematical details.
- Develop the ability to evaluate and improve the accuracy and performance of Machine Learning models.
- Explore the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks.