Hands-on Scikit-learn for Machine Learning: Tackle real-world Machine Learning problems using Scikit-learn to quickly build a portfolio of practical Machine Learning tools

Farhan Nazar Zaidi

S$176.99

5 (1 Ratings)

Video Aug 2018 9hrs 3mins 1st Edition

Farhan Nazar Zaidi

S$176.99

5 (1 Ratings)

Video Aug 2018 9hrs 3mins 1st Edition

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Download Code

Key benefits

Apply the Machine Learning techniques and processes and widely used functions Scikit-learn has to offer for a particular task.
With practical hands-on approach, build strong intelligent ML systems with minimal effort
Explore datasets from a diverse set of ML problem domains, applying different models and techniques.

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 at: https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V-

Who is this book for?

If you are a software developer, machine learning engineer, or data analyst and want to use Scikit-learn for different Machine Learning and analytics tasks, this course is for you. You need to have a very basic understanding of Machine Learning and Data Analytics. However, no knowledge of Scikit-learn is needed. Python programming knowledge and a basic understanding of Numpy and the Pandas library are assumed.

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

What do you get with a video?