https://github.com/jesussantana/ibm-machine-learning-with-python
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language
https://github.com/jesussantana/ibm-machine-learning-with-python
clustering data-science decision-trees dimensionality-reduction machine-learning python random-forests regression-models unsupervised-learning
Last synced: about 2 months ago
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This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language
- Host: GitHub
- URL: https://github.com/jesussantana/ibm-machine-learning-with-python
- Owner: jesussantana
- License: mit
- Created: 2021-05-29T08:51:44.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-30T11:06:50.000Z (about 5 years ago)
- Last Synced: 2025-02-28T22:51:37.144Z (over 1 year ago)
- Topics: clustering, data-science, decision-trees, dimensionality-reduction, machine-learning, python, random-forests, regression-models, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 1.37 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IBM Pyhton for Data Science
[](https://www.python.org/)
[](https://jupyter.org/try)
## This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
## Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
## Explore many algorithms and models:
- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
## ReferencesGet ready to do more learning than your machine!
## COURSE SYLLABUS:
### Module 1 - Supervised vs Unsupervised Learning
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
### Module 2 - Supervised Learning I
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
### Module 3 - Supervised Learning II
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
### Module 4 - Unsupervised Learning
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters - Single Linkage Clustering
- Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
- Density-Based Clustering
### Module 5 - Dimensionality Reduction & Collaborative Filtering
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
### PREREQUISITES
- Python for data science
## RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE
### You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.
### This hands-on lab requires that you have working knowledge of Python programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with Python, I recommend you take Data Analysis with Python courses.
https://cognitiveclass.ai/courses/machine-learning-with-python