Python Machine Learning Course, 1-Days, Online Attendance

Python Machine Learning 2-day 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:

• 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 Machine Learning Algorithms: Apriori, Euclat

Other machine learning Algorithms:

• Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting
• Reinforcement learning Algorithms: Q-Learning
• 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.

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.