Machine Learning with TensorFlow Training Course

Who should attend

Analysts, Data Scientists, and software developers who want to get a practical introduction to building Machine Learning solutions using TensorFlow.

Prerequisites

Delegates should have attended our intro to Python Programming training course or have equivalent experience of coding with Python, or have strong experience developing with another language such as C++, C#, Scala, JavaScript. A working knowledge of Linear Algebra and Vector Calculus would be very useful too.

Live, instructor-led online and on-site training

We appreciate that you need flexibility to fit in with new working situations - whether you're an individual, part of a distributed team, or simply have projects and deadlines to meet. 

Our remote training can take place online in a virtual classroom, with content split into modules to accommodate your scheduling challenges and meet your learning goals. Get in touch today to find out how we can help design a cost-effective, flexible training solution.

Machine Learning & Neural Networks Overview

• What is Machine Learning?
• Machine Learning problems and applications
• Neural Networks overview
• The Python Machine Learning ecosystem: TensorFlow vs scikit-learn

Machine Learning Basics

• Learning and predicting
• Supervised vs Unsupervised Learning
• Feature engineering, feature selection, feature scaling
• Training data and test data
• Cross-validation
• Evaluation metrics

Loading, exploring and pre-processing your data

• The tf.data API
• Working with NumPy arrays
• TensorFlow datasets

Building your first Neural Network model

• Choosing a network architecture
• The tf.keras API
• Setting up and compiling a model in TensorFlow
• Training the model
• Model evaluation
• Making predictions

TensorFlow demos

• Classification: predicting a label
• Text classification with TensorFlow
• Image classification with TensorFlow
• Regression: predicting a quantity
• Linear Regression with TensorFlow
• Improving prediction quality
• Error analysis
• Hyper-parameter tuning

Miscellanea

• Ethics in Machine Learning
• Discussion points on advanced application