SciKit-Learn Machine Learning for Magic Predictions with, 1-hour, Online

Python Machine Learning 1 hour Course

Prerequisites:

Basic knowledge of Python coding is a pre-requisite.

Who Should Attend?

This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.

Practical:

• We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project
• We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.

Course Outline:

Supervised Machine Learning

• What is supervised machine learning?
• Brif intro yo Classification Algorithms, with examples being demo-ed, comparisons and when to use:
• Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
• Regression Algorithms: Linear, Polynomial

Unsupervised Machine Learning:

• What is, with some of tje algorithms compared, how to choose the best one:
• Clustering Algorithms: K-means clustering, Hierarchical Clustering
• Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
• Association Machine Learning Algorithms: Apriori, Euclat

Other machine learning Algorithms:

• Ensemble Methods ( Stacking, bagging, boosting )

What is included in this Python Machine Learning:

• Python Machine Learning Certificate on completion
• Python Machine Learning notes
• Practical Python Machine Learning exercises and code examples
• After the course, 1 free, online session for questions or revision Python Machine Learning.
• Max group size on this Python Machine Learning is 4.