Keras for Machine Learning Online Course

Keras for Machine Learning Online Course

This hands-on course introduces you to deep learning in Python using Keras, a powerful library that simplifies working with Theano and TensorFlow. You’ll learn how to build, train, and evaluate neural networks through practical exercises, making Keras an ideal tool for rapid prototyping. Designed as a playbook and workbook, the course helps you learn by doing so you can confidently apply Keras to your own deep learning projects.

Who should take this course?

This course is ideal for aspiring data scientists, machine learning enthusiasts, AI developers, and Python programmers who want to build and train neural networks using Keras. It’s also perfect for students and professionals looking to strengthen their deep learning skills through hands-on projects.

What you will learn

• Develop and evaluate neural network models end-to-end
• Build larger models for image and text data
• Understand the anatomy of a Keras model
• Evaluate the performance of a deep learning Keras model
• Build end-to-end regression and classification models in Keras
• Learn how to use checkpointing to save the best model run

Course Outline

Introduction

• Introduction
• What You'll Learn in this Course
• Is this Course Right for You?
• What is Keras?

Foundations

• Theano
• TensorFlow
• Artificial Neural Network Anatomy
• Deep Learning
• Keras Life Cycle
• Keras Code Anatomy
• Demo: Case Study on Pima Indian Diabetes Dataset: Load Data
• Demo: Case Study on Pima Indian Diabetes Dataset: Define and Compile
• Demo: Case Study on Pima Indian Diabetes Dataset: Fit and Evaluate
• Performance Evaluation on Neural Networks
• Demo: Case Study on Data Segmentation
• Scikit-Learn for General Machine Learning
• Evaluate Models with Cross-Validation
• Grid Search Deep Learning Model Parameters
• Demo: Case Study on Multiclass Classification
• Demo: Case Study on Multiclass Classification: Part 2
• Demo: Case Study on Binary Classification
• Demo: Case Study on Binary Classification: Part 2
• Demo: Case Study on Binary Classification: Part 3
• Demo: Case Study on Binary Classification: Part 4
• Demo: Case Study on Regression
• Demo: Case Study on Regression: Part 2
• Demo: Case Study on Regression: Part 3

Going Deeper with Keras

• Model Serialization
• Save Neural Network to JSON
• Save Neural Network to YAML
• Demo: Case Study on Checkpointing
• Demo: Case Study on Checkpointing: Part 2
• Plotting History
• Visualize Model Training History in Keras
• Demo: Case Study on Dropping Out
• Demo: Case Study on Dropping Out: Part 2
• Dropout Tips
• Learning Rate Defined
• Configure Learning Rate
• Demo: Case Study on Learning Rates
• Demo: Case Study on Learning Rates: Part 2
• Demo: Case Study on Learning Rates: Part 3

Convolutional Neural Networks

• Convolutional Neural Networks
• Demo: Case Study on Handwritten Digit Recognition
• Demo: Case Study Handwritten Digit Recognition: Part 2
• Demo: Case Study on Handwritten Digit Recognition: Part 3
• Demo: Case Study on Handwritten Digit Recognition: Part 4
• Image Augmentation
• Demo: Case Study on Image Augmentation
• Demo: Case Study on Image Augmentation: Part 2
• Image Augmentation Tips
• Object Recognition
• Demo: Case Study on Object Recognition
• Improving Model Performance
• Sentiment Analysis in Keras
• IMDB Dataset Properties
• Word Embedding Defined
• Demo: Case Study on Word Embedding
• Demo: Case Study on Word Embedding: Part 2

Recurrent Neural Networks

• Recurrent Neural Networks
• Demo: Case Study on Time Series Prediction
• Demo: Case Study on Time Series Prediction: Part 2
• Demo: Case Study on Time Series Prediction: Part 3
• Demo: Case Study on Time Series Prediction with LSTM
• Demo: Case Study on Time Series Prediction with LSTM: Part 2
• Demo: Case Study on Time Series Prediction with LSTM: Part 3
• Demo: Case Study on Sequence Classification
• Demo: Case Study on Sequence Classification: Part 2