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Introduction to Machine Learning with Python drafted draft

Goldsmiths, University of London
London , United Kingdom
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Tuition Fee
USD 21,576
Per course
Start Date
Not Available
Medium of studying
On campus
Duration
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Details
Program Details
Degree
Courses
Major
Costume Design | Design | Directing | Ethics
Area of study
Information and Communication Technologies | Natural Science
Education type
On campus
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
USD 21,576
About Program

Program Overview

Course overview

This 10-week course is a practical introduction to machine learning using Python, one of the most widely used and in-demand programming languages. Taught live online, you’ll explore how to use statistical techniques and machine learning algorithms to create models that enable computer systems to learn from different types of data. These models can support decision-making in a range of fields, including market prediction, within scientific research and statistical analysis.

Open to anyone interested in machine learning and data science, this course is suitable for anyone who is interested in understanding the potential of machine learning when applied to datasets. The only requirement is a basic level of computer literacy, and this course is open to:

  • those with no prior knowledge or experience.
  • those with some coding experience.
  • experienced programmers.

    • The course is both theoretical and practical, and will ensure you understand the theory behind the algorithm, before you perform tests on real-world data examples. You'll be guided through the analysis of large amounts of data and classification of appropriate categories and taught how to recognise recurring features and identify correlations, so that you can develop a complex system which has the ability to make accurate predictions.

    You'll begin by exploring the main tools provided in Python for data visualisation and data analysis, learning how to process data and create graphs that can reveal specific insights of a database. You’ll then delve into the theory and practical application of the main machine learning algorithm in Scikit-learn for classification and regression models. Following this you’ll begin to explore the sophisticated machine-learning library, Keras, which will expand on your work with Scikit-learn. The course will also cover some of the other most popular Python data science libraries including Jupyter Notebook, NumPy, pandas and matplotlib. Towards the end of the course, you'll have the opportunity to discuss Neural Networks and Convolutional Neural Networks to give you a brief introduction to the concept of Deep Learning.

    Course structure

    Taught through practical examples and theoretical explanation, the course will cover the following key aspects of Machine Learning: Data Pre-processing, Regression, Classification, Clustering, Introduction to Deep Learning. Specific topics include:

  • Installation of the Anaconda Distribution for running Python code, presentation of how machine learning is applied today in the industry, plus Python review.
  • Importing your dataset and make operation on your data using Pandas Python Library. Theory on features analysis and best practice in understanding the data.
  • Data Visualisation in Seaborn. Exploration of the most useful data visualisation to extract information from the database.
  • Linear regression in theory and practice.
  • Logistic Regression in theory and practice.
  • K Nearest Neighbours in theory and practice.
  • Decision Trees and Random Forests.
  • Support Vector Machine.
  • Unsupervised learning: K means and clustering.
  • Introduction to Neural Nets and Deep Learning.

    • Learning outcomes

  • Learn new skills that can further your career and improve your career prospects; Data Scientist is one of the most trending jobs in many economic sectors, and machine learning applied to data science is increasingly adopted due to its ability to solve problems relating to the increased availability of large datasets.
  • Gain an understanding of the theory behind the algorithm and experience testing your knowledge on real-world data examples.
  • Build your confidence, by learning how to ensure that there are no erroneous representations and rules of operation that lead to errors or misjudgements in your machine learning models.

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