Machine Learning with Python Training Course

Who should attend

Analysts, Data Scientists, and software developers who want to get a practical introduction to Data Science and Machine Learning with Python.

Prerequisites

Delegates should ideally have some prior experience programming and / or of using statistical analysis techniques. Any experience with Python would be beneficial but we will give you "just enough" Python pointers to be able to create real-world workable solutions to data analysis and visualisation challenges.

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.

Overview of Machine Learning

• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up
  • The Anaconda Python distribution
  • Jupyter Notebooks
  • Python Ecosystem for Data Science and Machine Learning

Machine Learning Overview

• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting
• Supervised Learning Problems
  • Regression: predicting a quantity
    • Algorithm in depth: Linear Regression and Polynomial Regression
  • Classification: predicting a label
    • Algorithm in depth: k-Nearest Neighbours
    • Algorithm in depth: Support Vector Machine
    • Algorithm in depth: Naive Bayes
• Unsupervised Learning Problems
  • Clustering: grouping similar items
    • Algorithm in depth: k-Means
    • Algorithm in depth: Hierarchical Agglomerative Clustering
    • Algorithm in depth: DBSCAN
  • Dimensionality Reduction
    • Algorithm in depth: Principal Component Analysis
• Evaluation of Machine Learning algorithms

Deep Learning & Neural Network Overview

• Intro to Artificial Neural Networks
• Mathematical Concepts required by Deep Learning
• Neural Network concepts
  • Neural Network Types
  • Gradient Descend
  • Back-propagation
  • Activation Functions
  • Loss Functions
  • Hyper-parameters
• Deep Network Architectures
• Deep Learning Libraries