Machine Learning with Python

Description

In this course, you will learn:

• This Machine Learning with Python course covers the fundamentals of machine learning using an easy and well-known programming language.
• In terms of Supervised vs Unsupervised Learning, investigate the relationship between Statistical Modeling and Machine Learning and compare the two.
• Take a look at real-world instances of machine learning and how it influences society in unexpected ways!
• Explore a variety of algorithms and models:
• Popular algorithms include classification, regression, clustering, and dimensionality reduction.
• Popular models include train-test split, root mean squared error, and random forests.
• Prepare to perform more learning than your machine!

Syllabus:

1. Supervised vs Unsupervised Learning

• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning 
• Supervised Learning Classification 
• Unsupervised Learning 

2. Supervised Learning I

• K-Nearest Neighbors 
• Decision Trees 
• Random Forests
• Reliability of Random Forests 
• Advantages & Disadvantages of Decision Trees 

3. Supervised Learning II

• Regression Algorithms 
• Model Evaluation 
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models 

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 

5. Dimensionality Reduction & Collaborative Filtering

• Dimensionality Reduction: Feature Extraction & Selection 
• Collaborative Filtering & Its Challenges 

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