Machine-Learning-with-Scikit-learn

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Machine-Learning-with-Scikit-learn

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Machine Learning with Scikit-learn by Packt Publishing

Machine-Learning-with-Scikit-learn 최신버젼 다운로드

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# Machine Learning with Scikit-learn [Video]
This is the code repository for [Machine Learning with Scikit-learn [Video]](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-scikit-learn-video?utm_source=github&utm_medium=repository&utm_campaign=9781789134780), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the video course from start to finish.
## About the Video Course
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

<H2>What You Will Learn</H2>
<DIV class=book-info-will-learn-text>
<UL>
<LI>Review fundamental concepts such as bias and variance 
<LI>Extract features from categorical variables, text, and images&nbsp; 
<LI>Predict the values of continuous variables using linear regression and K Nearest Neighbors&nbsp; 
<LI>Classify documents and images using logistic regression and support vector machines 
<LI>Create ensembles of estimators using bagging and boosting techniques 
<LI>Discover hidden structures in data using K-Means clustering 
<LI>Evaluate the performance of machine learning systems in common tasks </LI></UL></DIV>

## Instructions and Navigation
### Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:<br/>
Familiarity with machine learning fundamentals and Python are helpful, but not required
### Technical Requirements
This course has the following software requirements:<br/>
Python 3.x

## Related Products
* [Machine Learning with scikit-learn and Tensorflow [Video]](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-scikit-learn-and-tensorflow-video?utm_source=github&utm_medium=repository&utm_campaign=9781788629928)

* [Fundamentals of Machine Learning with scikit-learn [Video]](https://www.packtpub.com/big-data-and-business-intelligence/fundamentals-machine-learning-scikit-learn-video?utm_source=github&utm_medium=repository&utm_campaign=9781789134377)

* [Real-World Machine Learning Projects with Scikit-Learn [Video]](https://www.packtpub.com/big-data-and-business-intelligence/real-world-machine-learning-projects-scikit-learn-video?utm_source=github&utm_medium=repository&utm_campaign=9781789131222)

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