Hands-on Machine Learning and Deep Learning with Python - scikit-learn, LightGBM, Keras / TensorFlow

Course Details

• Duration: 2 days
• Start Date: September 29, 2022
• End Date: September 30, 2022
• Level: Beginner to Intermediate
• Format: Hands-on workshop with practical exercises
• Technologies: Python, scikit-learn, LightGBM, TensorFlow, Keras, PyTorch

Course Overview

Learn machine learning fundamentals and implementation through lectures and hands-on exercises.

Complex mathematics — often a barrier — is limited to what is necessary, so you can efficiently and systematically build knowledge and skills over two practical days. You will learn widely used libraries such as scikit-learn and LightGBM, plus popular deep learning frameworks such as TensorFlow / Keras, and practice the full process: data handling, model building, evaluation, and tuning.

Learning Objectives

• Explain the fundamentals of machine learning
• Implement machine learning models with scikit-learn
• Implement machine learning models with LightGBM
• Implement deep learning with TensorFlow / Keras
• Implement image classification models with TensorFlow / Keras

Course Modules

Module 01: Machine Learning Overview

• Machine learning fundamentals
• Process for using and building machine learning
• Machine learning development environments

Module 02: Linear Regression — scikit-learn

• Linear regression
• Implementing linear regression models
• Improving models with feature selection
• Exercise: linear regression

Module 03: Logistic Regression — scikit-learn

• Logistic regression
• Implementing logistic regression
• Improving models with standardization
• Exercise: logistic regression

Module 04: Decision Trees and Random Forest — scikit-learn

• Decision trees
• Implementation and parameter tuning
• Optional exercise: decision trees
• Random Forest
• Implementation and parameter tuning
• Optional exercise: Random Forest

Module 05: Gradient Boosting — LightGBM

• Gradient boosting trees
• LightGBM implementation and parameter tuning
• Cross-validation and grid search
• Exercise: LightGBM and grid search

Module 06: Deep Learning — TensorFlow / Keras

• Deep learning
• TensorFlow / Keras
• Exercise: deep learning
• Exercise: applying ML processes to structured data
• Examples of machine learning on structured data

Module 07: Image Classification with CNN — TensorFlow / Keras

• Convolutional neural networks (CNN)
• CNN image classification with TensorFlow / Keras
• Exercise: CNN image classification
• Examples of image classification use cases

Module 08: Appendix: Deep Learning Implementation with PyTorch