WA2906

Machine Learning Workshop Using Tensorflow 2.0 and Keras Training

Do you need to solve business problems using Machine Learning? In this workshop-based course, you will learn the fundamentals of Machine Learning and Neural Networks — and how you can apply them directly into the workplace. Using both Tensorflow 2.0 and Keras training, each workshop is designed with practicality in mind, ensuring concepts are real-world focused.

Introduction Course Outline

This course will help you build a strong foundation in deep learning using Keras and Tensorflow. Hands on workshop style projects are used to teach the material. These projects try to represent real life problems, including collecting and cleaning up data, designing a model, training the model with data, and starting with prediction.

Prerequisites

Basic programming experience in any language is needed. You will receive enough introduction to Python to complete this course.
Knowledge of calculus and linear algebra is recommended but not necessary.

Duration

Two days

Outline for Machine Learning Workshop Using Tensorflow 2.0 and Keras Training

Workshop 1 - Tensorflow Basics

Learn about Python language, Numpy, Pandas and Tensorflow.

Workshop 2 - Gradient Descent

Learn how GD works and how machines learn using this technique.

Workshop 3 - Simple Linear Regression

Perform linear regression in a very simple problem domain. The goal is to learn how linear regression works.

Workshop 4 - Ames, Iowa House Price Prediction Using Neural Network

Learn the theory behind neural networks.

Apply neural network to solve a real life regression problem.

Workshop 5 - AirBnB Rent Prediction

This is a realistic regression problem. We try to predict property rental prices in the Boston area. We learn to work with categorical features like neighborhood and property type.

This workshop also shows the common techniques used to preprocess data.

Workshop 6 - Lung Capacity Prediction

This is a selfguided workshop. You will be given the dataset and the problem description.

You will need to solve the problem using a neural network.

Workshop 7 - Logistic Regression Using Gradient Descent

Learn the theory behind logistic regression (or classification).

Solve a simple classification problem using Gradient Descent.

Workshop 8 - Titanic Survivability Prediction

Solve a realistic classification problem using a neural network.

Workshop 9 - Fetal Monitoring Complication Prediction

Learn the theory behind multi-class classification.

Solve a medical classification problem using a neural network.

Workshop 10 - Credit Card Fraud Detection

In this workshop we get deeper into evaluating the quality of a model.

We learn about Confusion Matrix, Precision and Recall.

Workshop 11 - Epileptic Seizure Recognition

This is a selfguided workshop. You will be given the dataset and the problem description.

You will need to solve the problem using a neural network.

Workshop 12 - Basic Convolutional Neural Network (CNN)

The goal of this workshop is the understand the structure of a CNN. We learn about the convolution layer, max pooling layer, fully connected layer and readout layer. We solve the MNIST handwritten digit comprehension problem.

Workshop 13 - Theory of Convolutional Neural Network

Learn the theory behind matrix convolution. Observe how convolution works on images.

Workshop 14 - Handwritten Digit Recognition

Apply CNN to classify handwritten digit images.

Workshop 15 - Solve CIFAR-10 Challenge

This is a selfguided workshop.

CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes. We train a CNN that tries to classify images in those 10 classes.