scikit-learn Cookbook Over 80 recipes for machine learning in Python with scikit - learn, 3rd Edition

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Free Download scikit-learn Cookbook: Over 80 recipes for machine learning in Python with scikit-learn, 3rd Edition
English | 2025 | ISBN: 1836644450 | 388 pages | True PDF,EPUB | 19.86 MB
Get hands-on with the most widely used Python library in machine learning with over 80 practical recipes that cover core as well as advanced functions

Key Features
Solve complex business problems with data-driven approaches
Master tools associated with developing predictive and prescriptive models
Build robust ML pipelines for real-world applications, avoiding common pitfalls
Book Description
Trusted by data scientists, ML engineers, and software developers alike, scikit-learn offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling the efficient development and deployment of predictive models in real-world applications. This third edition of scikit-learn Cookbook will help you master ML with real-world examples and scikit-learn 1.5 features.
This updated edition takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you'll explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn.
By the end of this book, you'll have gained the knowledge and skills needed to confidently build, evaluate, and deploy sophisticated ML models using scikit-learn, ready to tackle a wide range of data-driven challenges.
What you will learn
Implement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using scikit-learn
Perform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performance
Optimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliability
Deploy ML models for scalable, maintainable real-world applications
Evaluate and interpret models with advanced metrics and visualizations in scikit-learn
Explore comprehensive, hands-on recipes tailored to scikit-learn version 1.5
Who this book is for
This book is for data scientists as well as machine learning and software development professionals looking to deepen their understanding of advanced ML techniques. To get the most out of this book, you should have proficiency in Python programming and familiarity with commonly used ML libraries; e.g., pandas, NumPy, matDescriptionlib, and sciPy. An understanding of basic ML concepts, such as linear regression, decision trees, and model evaluation metrics will be helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability will also be invaluable.

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Machine Learning with SciKit-Learn with Python
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 3.59 GB
Genre: eLearning Video | Duration: 54 lectures (8 hour, 23 mins) | Language: English

Get a practical understanding of the Scikit-Learn library and learn the ML implementation​

What you'll learn

This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn.
You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library.

Requirements

Several topics or concepts are there for which you should have a basic understanding of to make the learning of this library easy for you. The very first thing is the basics of python. As this library is entirely based on python, the trainees need to have a basic understanding of the concepts of python. If you would have worked with python, you will find the concepts covered here pretty simple.
The next important concept is the basics of Machine learning. With the help of this library, we will be implementing the concepts of ML. So it is very necessary to understand how it could be used. In this Scikit-learn Training, we have included all the topics that we are considering as the prerequisite here so that the trainees can brush up their understanding before beginning this training.

Description

The goal of this course is to help the trainee's expertise working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand. You will understand how this library helps the application by helping the developers in adding machine learning-based concepts. After the mid part of the video, you will be learning about the topics that fall under the court of advanced level concepts. After this unit, you will be able to work to implement the concepts of Machine learning with the help of SciKit-Learn.

Scikit-learn can be defined as the python based library which is used to implement the concepts of machine learning in the application. It could also be explained as the predefined set of functions that is leveraged to bring the features in the application which are considered linked with machine learning. It is the library that consists of various tools for statistical modeling and machine learning. Regression, clustering, and classification are some of the most useful tools that could be found in this library. It is built on top of NumPy, SciPy, and Matplotlib which is one of the reason behind the functions it provides. Being based on python, it will only be supported while implementing things using the python programming language. It can be used the same way as other libraries are used in python but the features it will offer will be unique and focused on Machine learning.

Who this course is for:

This course is open to all who want to master working with this library. We have developed the course in a manner so that I could have something for any sort of audience. The students who want to grow their career in python and want to learn about this library can be the best target audience for this course.
The developers who are working in other programming languages and want to jump to Python to begin working with Machine learning can be the best target audience for this course. They will be learning about this library in a very detailed manner and will also learn how to implement this in python.
The educators who are training folks in python or machine learning can also be the best target audience for this Scikit-learn Training. They will be learning about this library very deeply and will be able to deliver their understanding to their trainees.

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