https://github.com/trainingbypackt/machine-learning-fundamentals-elearning

Use Python and scikit-learn to get up and running with the hottest developments in AI
https://github.com/trainingbypackt/machine-learning-fundamentals-elearning

artificial-intelligence clustering decision-tree machine-learning neural-network python scikit-learn supervised-learning unsupervised-learning

Last synced: 2 months ago
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Use Python and scikit-learn to get up and running with the hottest developments in AI

• Host: GitHub
• URL: https://github.com/trainingbypackt/machine-learning-fundamentals-elearning
• Owner: TrainingByPackt
• License: mit
• Created: 2019-02-26T04:30:48.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-02-26T04:48:12.000Z (over 7 years ago)
• Last Synced: 2025-01-13T20:14:28.471Z (over 1 year ago)
• Topics: artificial-intelligence, clustering, decision-tree, machine-learning, neural-network, python, scikit-learn, supervised-learning, unsupervised-learning
• Language: Jupyter Notebook
• Size: 903 KB
• Stars: 1
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Machine Learning Fundamentals eLearning

This course will begin by learning how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. Then, the focus of the course shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve performance of the algorithm by tuning hyperparameters. When it finishes, this course would have given you the skills and confidence to start programming machine learning algorithms.

## What you will learn

* Understand the importance of data representation

* Gain insight into the difference between supervised and unsupervised models 

* Explore the data using the Matplotlib library

* Study popular algorithms, such as K-means, Gaussian Mixture, and Birch

* Implement a confusion matrix using scikit-learn

* Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM

* Visualize errors in various models using matplotlib

### Hardware requirements

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

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

* **Memory**: 4GB RAM or higher 

* An Internet connection

### Software requirements

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

* Sublime Text (latest version), Atom IDE (latest version) or other similar text editor applications.

* [Python 3](https://www.python.org/downloads/)

* The following python libraries installed: NumPy, SciPy, scikit-learn, Matplotlib