https://github.com/trainingbypackt/hands-on-machine-learning-with-python

Structure, build, and deploy machine learning projects
https://github.com/trainingbypackt/hands-on-machine-learning-with-python

machine-learning numpy pandas python scikit-learn scikitlearn-machine-learning

Last synced: 11 months ago
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Structure, build, and deploy machine learning projects

• Host: GitHub
• URL: https://github.com/trainingbypackt/hands-on-machine-learning-with-python
• Owner: TrainingByPackt
• License: mit
• Created: 2019-03-12T10:05:24.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-05-09T01:38:37.000Z (almost 7 years ago)
• Last Synced: 2025-01-13T20:14:28.325Z (about 1 year ago)
• Topics: machine-learning, numpy, pandas, python, scikit-learn, scikitlearn-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 12.1 MB
• Stars: 0
• Watchers: 5
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Hands-On-Machine-Learning-with-Python

Structure, build, and deploy machine learning projects

# Hands-On Machine Learning with Python

This course gives students basic ideas behind machine learning methods as well as a deeper understanding of how and why they work. Emphasis is placed on how to get these algorithms to work in practice, rather than focusing on mathematical derivations. Through a project-based approach, the course gives students the opportunity to implement algorithms themselves and gain experience with them. The course covers various machine learning techniques for both supervised and unsupervised learning approaches. It also goes further to teach students about deploying a model into production. 

# Hands-On Machine Learning with Python by **Brent Kievit-Kylar, Kiran N Kumar and Ramya Rao**

## What you will learn

* Use scikit-learn, pandas, numpy library to perform machine learning and data analysis tasks

* Obtain, verify, clean and transform data into correct format for use

* Perform exploratory analysis and extract features from the data.

* Build models for regression, classification and clustering tasks.

* Evaluate the performance of a model with the right metric

* Deploy a final machine learning model into production 

### Hardware Requirements

For an optimal student experience, we recommend the following hardware configuration:

* **Processor**: Intel Core i5 or equivalent

* **Memory**: 4 GB RAM

* **Storage**: 35 GB available space

### Software Requirements

You'll also need the following software installed in advance:

* **OS**: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit

* **Browser**: Google Chrome, Latest Version