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Introduction to Data Science & Machine Learning

27-29 April 2020 | New York

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Course Overview

Machine learning is a subset of artificial intelligence (AI) that provides systems with the ability to learn and improve from experience, without a need for explicit instructions. while a simple concept, machine learning has opened a world of possibilities and today it being used across various industries for a multitude of functions whether this be improving customer experiences through making personalised recommendations to predicting future values of a stock price.

This hands-on class is designed for those wanting to get a start in data science and machine learning and will walk you through the core python data science tools, the Scikit-Learn machine learning toolkit, and explore practical concerns for machine learning with python, including data cleansing, pre-processing, and evaluation.

Early Bird: £1,395(book by 27th Mar)

New York: 27-29 April 2020

PRICE: $1,695


Why choose this course?

Intensive Crash-Course

At the end of the course you will have a solid foundation in machine learning and data science. Your understanding will be put to the test on day 3 in which you will participate in a 'machine learning hackathon.

Programme Modules

  • The Python data science stack
  • Introduction to machine learning & model building
  • Python machine learning with Scikit-Learn models
  • Preprocessing
  • Measuring performance
  • ML Hackathon - to bring it together

Supporting Materials

  • Online Learning Module: Introduction to Python Programming
  • Free Book: Data Science and Machine Learning: Mathematical and Statistical Methods

Learning Outcomes

At the end of this programme delagates will be able to:

  • Have hands-on experience with core Python data science, visualization, and machine learning tools
  • Understand supervised vs unsupervised machine learning
  • Learn how to build classification and regression decision trees
  • Understand how to prepare data for machine learning, including pre-processing, cleansing, and feature selection and engineering
  • Be able to use cross-validation to evaluate the performance of your machine learning models

Benefits for the organisation

  • Enhance customer experiences by modernizing existing applications such as recommenders, search ranking and time series forecasting
  • Make better decisions from automated data visualization
  • Save time and money by automating repetitive tasks

Programme Modules

Module 1

The Python data science stack

  • Pandas for data manipulation
  • Numpy to speed up Python numerical processing
  • Matplotlib for visualization

Module 2

Introduction to machine learning & model building

  • Conceptual overview
  • Supervised vs unsupervised
  • Regression vs classification
  • Variance/bias tradeof

Module 3

Python machine learning with Scikit-Learn models

  • Linear models
  • Trees & forests
  • k-nearest neighbours
  • k-means clustering

Module 4


  • Advanced Pandas data-munging
  • Dealing with dates and feature engineering
  • Encoding categorical data
  • Scaling inputs
  • Dimensionality reduction

Module 5

Measuring performance

  • Train/test split
  • Tuning models with cross-validation

Module 6

ML Hackathon - to bring it together

The last day will be a hands-on project where students will form small teams and work on a solution to a machine learning problem, applying the ideas and tools they've learned during the course.