Python Machine Learning with Scikit Learn Training

SkillsFuture Course - Python Machine Learning with Scikit Learn Training 
This training aims to equip you with basic machine learning knowledge using Python scikit-learn package such as regression, classification, clustering, decision trees and neural networks. The topics include:

• Supervised Learning
• Classification
• Linear regression
• Unsupervised Learning
• Clustering
• Principal Component Analysis (PCA)
• Decision Tree
• Basics of Neural Network

Module 1 Getting Started on Scikit-Learn

• What is Machine Learning
• Machine Learning Steps
• What is Scikit-Learn
• Installing Scikit-Learn

Module 2 Datasets

• What is Dataset 
• Iris Dataset
• Handwritten Digits Dataset
• Boston Housing Price Dataset
• Splitting Datasets for Training/Testing

Module 3 Supervised Learning

• What is Supervised Learning
• Key Classifiers Algorithms - KNN, SVM, GNB, DT, Ensemble
• Performance Metric and Errors
• Model Persistence
• Regression

Module 4 Unsupervised Learning

• What is Unsupervised Learning
• Key Clustering Algorithms - K-Means, Mean Shift, Agglomerative
• Dimensionality Reduction - PCA

Module 5 Neural Network

• What is Neural Network?
• Multi Layer Perceptron Classifier
• Hidden Layers
• Activation Function
• Solver
• Learning Rate
• Momentum

For more details, please visit our website at https://www.tertiarycourses.com.sg/python-machine-learning-with-scikit-learn-training.html or contact us at [email protected]

Jan Idziak has 5 years of industrial and academic experience in Data Science, Deep Learning, and Data Visualizations. This year, he was invited to the Country of Australia to conduct and moderate Statistical Data Analysis with the Mathematics in Industry Study Group (MISG) in association with University of South Australia.Working across wide range of projects and sectors, he has designed various statistical models and machine learning tools, including portfolio pricing engines, customer segmentation systems, or product recommenders. When he worked in Business Intelligence department in Banking sector, as well as for United Nations, he was transforming raw data into meaningful and actionable insights. During his time as a president of the Financial Engineering Association, he organized few conferences and provided support and training for other members of the association. He has been providing coaching on topics such as scoring models, predictive modelling, personalized recommendation systems, deep learning, data visualization, or natural language processing;

Jan specializes in data analysis, visualization, and R package development. His main technical skill set consists of languages/tools like Python, SPSS, Statistica, Matlab, R, JS, D3, html, R (including shiny, Rmarkdown, and package development). In addiction, he has strong knowledge on most of the data mining algorithms, modern visualization and summarizing techniques.