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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

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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

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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

Preprocessing

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.