Python Machine Learning Course, 1-Days, Online Attendance

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.

Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine

Regression Algorithms: Linear, Polynomial

Clustering Algorithms: K-means clustering, Hierarchical Clustering

Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)

Association Machine Learning Algorithms: Apriori, Euclat

Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting

Reinforcement learning Algorithms: Q-Learning

Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)

The first part of a Machine Learning project understands the data and the problem at hand.

Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.

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.