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<b>Machine Learning - Data Science & Analytics for Developers (Full Course)</br>with Phil Winder</b></br>TBA

Machine Learning - Data Science & Analytics for Developers (Full Course)
with Phil Winder

TBA

£1,360.00 £1,600.00

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GOTO Academy are excited to bring you UK-based Phil Winder of Winder Research, for an intensive 3-day Data science and Analytics course, that will leave you with practical tools for utilizing Machine Learning principles in your organisation.

This course combines beginner, intermediate and advanced courses into one efficient program. At the end of this course you will be considered professionally capable of developing and delivering data science products. Immediately start applying what you've learnt!

The 3-day course can be reduced to a consecutive 2-day course depending on your skill level and your learning goals. If you're unsure which package is right for you, please contact us.

WHO WILL BENEFIT 

This course is aimed towards developers, in which we will delve into the mathematics behind the code as well as developing real life algorithms in Python. One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor.

PREREQUISITES

This is a full beginner to advanced level course, which is suitable for most users with some development experience. Some experience of Python is helpful, but not necessary. No data science experience is expected.

If you are a developer with some experience with data science you are welcome to join from Day 2.

COURSE STRUCTURE 

Each day will comprise of a series of sub-hour theoretical sessions separated by practical exercises. The expected learning outcomes for each day are as follows:


WHAT WILL YOU ACHIEVE ON DAY 1 

  • Discuss the differences between types of learning
  • Describe problems in a way which can be solved with Data Science
  • Understand the difference between regression and classification
  • Solve problems using regression algorithms
  • Solve problems using classification algorithms
  • Learn how to avoid overfitting and appreciate generalisation
  • Develop features within data
  • Describe how and where to obtain data

    TOPICS COVERED

    • How data science fits within a business context
    • Data science processes and language
    • Information and uncertainty
    • Types of learning
    • Segmentation
    • Modelling
    • Overfitting and generalisation
    • Holdout and validation techniques
    • Optimisation and simple data processing
    • Linear regression
    • Classification and clustering
    • Feature engineering
    • An in-depth practical example demonstrating the day’s concepts


    WHAT YOU WILL LEARN ON DAY 2

    • Evaluate models numerically
    • Investigate and assess models visually
    • Have practical experience in industrial statistics
    • Further enhance data pre-processing skills
    • Understand unsupervised learning
    • Gain experience in a wide variety of Machine Learning algorithms

    TOPICS COVERED

    • Numerical and visual model evaluation
    • Introduction and application of statistics in data science
    • Understand the practical steps to design and deploy models
    • Further experience with real-life messy data
    • Unsupervised Machine Learning
    • A range of Machine Learning models: e.g. Logistic regression, linear and nonlinear SVMs, decision trees, etc.
    • Introduction to tooling, testing and deployment
    • An in-depth practical example demonstrating the day’s concepts

    WHAT WILL YOU ACHIVE ON DAY 3

    • Develop solutions to mine, analyse and classify text
    • Discuss and explain neural networks, deep learning and a range of topologies
    • Employ semi-supervised machine learning to complex problems
    • Use ensemble methods to create cutting-edge machine learning products

    TOPICS COVERED

    • Text feature engineering
    • Text mining, representation and learning
    • Neural networks
    • Deep belief networks
    • Stacked denoising autoencoders
    • Convolutional neural networks
    • Semi-supervised machine learning
    • Ensemble methods
    • An in-depth practical example demonstrating the day’s concepts

    TRAINER: PHIL WINDER

    Phil Winder is a multi-disciplinary freelance architect working towards the research and development of cutting-edge technology.

    Most recently he has been developing cloud-based full-stack microservice systems for a range of clients but has a significant past in machine learning and electronics.

    His company, Winder Research has recently released a range of developer and business focused Data Science training courses. Visit WinderResearch.com to find out more.

    Phil has PhD and Masters degrees from the University of Hull, UK in Electronics, with a focus on embedded signal processing

    DETAILS

    Address London 
    Date: TBA
    Duration: 3 days. All days 9:00 to 17:00
    Price: 1'600 GBP incl. VAT and meals
    Discounts: Group discount 3+: 10%, 6+ 15% Please Contact us for group discounts.
    Payment: Card payment online or invoice option.
    Registration: Please either "Add to Cart" here on the website or write an email to mto@trifork.com. You will receive an invoice and a confirmation after registering. 


    CANCELLATION POLICY

    We do not provide any refunds. What happens in case you cannot attend the course?
    1) You are welcome to pass on the place to a colleague or
    2) You are welcome to attend a later course in our course calendar.
    Further, once a registration has been made and the confirmation email has been sent out, the price is set and can not be changed or adjusted.
     


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