Data Science and Machine Learning with Python

Want to harness the power of big data using data science and machine learning? This five-day instructor-led course is the perfect start.
5 day course
Supporting material
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 five-day course will guide you through the world of data science (DS) and machine learning (ML) using the Python programming language and data management principles. At the end of the course you will 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.

Through a combination of lectures, demonstrations and exercises, you will gain practical experience applying DS and ML techniques to real data.  This course is essential for individuals who want to leverage the power of data science and machine learning to drive organisation development.

Course overview
Who should attend:
  • Python developers
  • Coders looking to upskill and focus on big data
Walk away with the ability 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 visualizations
  • Gain actionable insights from big data
  • Confidently assess structure and quality of the 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
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
Module 1: 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
Module 2: Data Science -Preaparation and Actionable Insights
  • Variable types
  • Correcting data
  • Completing data
  • Creating data (feature engineering)
  • Converting data
  • Practical - real-life data set
Module 3: Machine Learning - Theory and Model Selection
  • Introduction to ML
  • Foundations - linear and logistic regression
  • Cost, loss, bias and variance
  • Usuing scikit learn
  • Practical - linear regression in practice
  • Commonly used ML models
Module 4: 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
Module 5: 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
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