About the course:
Our instructor-led TensorFlow training course will show you how to obtain, configure and deploy TensorFlow, and create your own Machine / Deep Learning solutions.
You will learn how to get your data ready to begin creating Neural Network Models to classify natural text, images and learn to solve regression problems.
We'll talk about the ethics of AI too, as this is an area often overlooked.
Remote / on-site options available - as part of a wider training programme or standalone workshop, as a custom on-site course, and public scheduled courses in London.
By the end of this course, you will have learnt about:
- The core concepts of Machine Learning, Neural Networks and Deep Learning
- Exploring and pre-processing your data for Machine Learning
- Building your first Neural Network model
- Using TensorFlow to tackle text classification, image classification and regression problems
- Improving on your models with tuning and error analysis
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.
As soon as it's safe, we'll return to also offering the on-site custom training courses and programmes upon which we've built our reputation.
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
