Machine Learning with Python

Learn the foundations of algorithms for predictive analytics, and discover how to apply them to your data using Python.

Description of the course

The Python Machine Learning syllabus is designed to help analysts, researchers, BI experts and developers becoming familiar with the implementation of Machine Learning solutions, through the use of tools in the Python programming language ecosystem.

Using a mix of frontal presentation and interactive examples, the course provides a comparison between Supervised and Unsupervised Learning, and offers an overview on core algorithms for predictive analytics, tackling tasks such as classification, clustering, regression analysis and dimensionality reduction. Notions of Neural Networks and latest developments in Deep Learning are also discussed.

Learning objectives

By attending this course, you will learn about:

  • Framing a business application as a Machine Learning task
  • The role of labelled data, data cleaning, and data transformation in Machine Learning systems
  • Feature engineering techniques to extract useful attributes from your data
  • The implementation of supervised and unsupervised learning techniques using Python
  • How to evaluate the quality of your models


Overview on the course
  • 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 with Anaconda Python
Machine Learning overview
  • Learning and Prediction
  • Feature Engineering
  • Training data and Test data
  • Cross-validation
  • Underfitting and Overfitting
Supervised Learning Problems
  • Classification: predicting a label
  • Algorithms for classification: k-Nearest Neighbours, Support Vector Machine and Naive Bayes
  • Regression: predicting a quantity
  • Algorithms for regression: Linear Regression
Unsupervised Learning Problems
  • Clustering: grouping similar items
  • Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
  • Dimensionality Reduction
  • Algorithms for dimensionality reduction: Principal Component Analysis
Evaluation of Machine Learning Algorithms
  • Evaluation metrics for machine learning
  • Planning an evaluation campaign on your data
Deep Learning & Neural Networks Overview
  • Intro to Artificial Neural Networks
  • Neural Network concepts
    • Neural Network Types
    • Gradient Descend
    • Back-propagation
    • Activation Functions
    • Loss Functions
    • Hyper-parameters
  • Neural Networks in the Wild: examples of successful applications
  • Deep Network Architectures
  • Deep Learning Libraries

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