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

IBM Pyhton for Data Science

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