Python Machine Learning | Cambridge Spark

LEVEL: INTERMEDIATE

DURATION: 2-DAY COURSE

DELIVERED: AT YOUR OFFICE

What you will learn

You will learn advanced state-of-the art machine learning techniques that are in demand in industry and research.

Model evaluation and optimisation (grid search)

Decision trees and random forests

Logistic regression

SVMs (linear SVMs, kernel trick, nonlinear SVMs)

Neural networks

Deep learning (local and cloud-based)

Languages and libraries :

Python 3

Numpy and Pandas for data manipulation

Scikit-learn for machine learning algorithms

Keras for neural networks and deep learning

Good knowledge of python, some familiarity with matrices, basic understanding of machine learning practice.

Individuals who wish to take their data science skills further and learn state-of-the-art techniques in this constantly evolving field.

Get in touch with us to learn more about the course!

DAY ONE

Introduction to Machine Learning
- Overview of Machine Learning
- Supervised vs. Unsupervised Learning
- Industrial Applications
Random Forests
- Decision Trees
- Ensemble models and Random Forests
- Overfitting, validation and the bias-variance trade-off
- Hyperparameter tuning, grid search and model selection
Support Vector Classifiers
- Linear SVCs
- The kernel trick and non-linear SVCs

DAY TWO

Neural Networks
- Overview of modern applications of Neural Networks
- The Perceptron
- Structure of general neural networks
- Training of Neural Networks
Deep Learning
- Motivation and architecture
- Real-world examples
- Convolutional Neural Networks
- Impact and limitations of Deep Learning

We will email you within the next 24 hours to arrange a quick call to help with any questions about the programme and recommend pre-course materials.

We look forward to speaking with you.

Director