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 code to get predictions and get accuracy scores
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.
