Python Machine Learning, online instructor-led

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

Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
Association Algorithms: Apriori, Euclat
Other machine learning Algorithms: Reinforcement learning Algorithms: Q-Learning

Ensemble Methods Algorithms: Stacking, bagging, boosting.

Random Forest Random Forest, Gradient Boosting

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

Data Exploration and Preprocessing: 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.

Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.