Hands-on Scikit-learn for Machine Learning

Rating 3.58 out of 5 (6 ratings in Udemy)

What you'll 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 …

Rating 3.58 out of 5 (6 ratings in Udemy)

What you'll 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

Description

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.

Duration 9 Hours 58 Minutes

Paid

Self paced

Intermediate Level

English (US)

Rating 3.58 out of 5 (6 ratings in Udemy)

Go to the Course
We have partnered with providers to bring you collection of courses, When you buy through links on our site, we may earn an affiliate commission from provider.