5 day course

Supporting material

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Data science and machine learning are two of the most important disciplines to emerge in technology in recent years, and at their heart is the understanding and management of data.

This course will guide you through the world of data science (DS) and machine learning (ML) using the Python programming language and data management principles. Through a combination of lectures, demonstrations and exercises, you’ll gain practical experience applying DS and ML techniques to real data, so it’s perfect for individuals who want to leverage the power of data science and machine learning to drive organisation development.

By the end of the five days, you’ll have a thorough understanding of analytics and automation, and will have developed practical skills in Python targeted towards data analysis so you can create sophisticated models.

Our Data Science and Machine Learning with Python course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in Australia.

Course overview

Who should attend:

This course is perfect for you if you’re a Python developer, or if you’re a coder who is looking to upskill and focus on big data.

What you'll learn:

By the end of this course, you will be able to:

Set up a powerful environment for data science and machine learning tasks
Load, visualise and clean big data
Output and describe descriptive statistics
Create a range of graphs and data visualisations
Gain actionable insights from big data
Confidently assess the structure and quality of data
Carry out thorough exploratory data analyses
Define and apply a range of machine learning algorithms
Understand the end-to-end process of machine learning, from data to prediction
Use correct machine learning algorithms based on data criteria
Optimise machine learning models and assess efficacy
Describe the process, underlying maths, architecture and applications of neural networks
Create, optimise and apply your own deep learning models for image recognition

Prerequisites

To get the most of out of this course, you should have:

A working knowledge of Python. You should be confident with basic variable types, functions, if/else, for loops and importing modules
Basic maths skills: understanding of mean, standard deviation, x/y graphs, logistic and linear regression are a plus

Course agenda

Introduction to Data Science

Basics of data science
Getting set up for data science
Introduction to jupyter notebook and gaggle
Numpy and pandas for data science
Loading and exploration of data
Visualisation techniques for data science
Practical: creating your first data science report

Data Science: Preparation & Actionable Insights

Variable types
Correcting data
Completing data
Creating data (feature engineering)
Converting data
Practical: real-life data set

Machine Learning: Theory & Model Selection

Introduction to ML
Foundations: linear and logistic regression
Cost, loss, bias and variance
Using scikit learn
Practical: linear regression in practice
Commonly used ML models

Applying Machine Learning Models

Real-world use cases
Practical: choosing and applying an ML model
Assessing results
Improving model output
Practical: your end-to-end ML project

Deep Learning: Applying Neural Networks

A conceptual introduction to deep learning and neural networks
Underlying maths
Neural network architecture
Practical: applying NNs
Optimising NNs
Image classification using neural networks
Taking these skills forward