• Machine Learning in Python training

  • Group Training

    Large amounts of data can contain many insights. These insights provide added value for business operations. You want to do this via automatic processes, namely with Machine Learning.

    Training code
    CGAMLIPYCD
    Spoken Language
    Dutch
    Language Materials
    English
    Dayparts
    4
    Price
    €1.400,00
    excl. VAT No extra costs.

    Book Machine Learning in Python now

    In group training, we use several learning methods to help you obtain the knowledge, give you helpful insights and get you inspired. Check the Spoken language and Language materials on the left for language info.

    This course currently isn't planned. Please fill in your contact details below and we'll get in touch with you within two working days.

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    What is Machine Learning in Python

    Large amounts of data can contain many insights. These insights in turn provide added value for business operations. But finding and checking these insights manually takes a lot of time. You would rather do this via a smart automatic process, namely with Machine Learning.
    Machine Learning in Python teaches you how to set up Machine Learning processes in Python, the most widely used programming language in Data Science.
    You will be introduced to terms such as
    CRISP-DM

    • Scikit-learn
    • K-fold Cross-Validation
    • Random Forest
    • K-means Clustering
    • Quantile Regressors
    • Convolutional Neural Network

    We will discuss different algorithms within Machine Learning and how you can apply them in Python. We also discuss how to build a Machine Learning pipeline in Python, how to ensure high data quality and how to detect and prevent model drift.
    During the training, the application of what you learn is central and you are challenged to work with the theory in practical cases. The training is given by people who work in practice with Machine Learning systems in Python and who know what it is about. They want to pass on that practical knowledge.

     
     

    Who should attend Machine Learning in Python

    The Machine Learning in Python training is suitable for:

    • Starters at the start of their career
    • People making a career switch to Data Science
    • People working in IT, especially in departments such as Data Science
    • Business Analysts
    • Information Analysts

    Prerequisites

    Beginning skills and general knowledge of Python. The 'Introduction Python' course addresses these topics and is ideally suited for pre-training.
    With this training you need a laptop.

    Objectives

    After this training, the participant is able to answer the questions:

    • How do you set up a Machine Learning pipeline in Python?
    • What are the advantages and disadvantages of different Machine Learning algorithms?
    • How do you extract insights from large amounts of data?
     
    Incompany

    Large amounts of data can contain many insights. These insights provide added value for business operations. You want to do this via automatic processes, namely with Machine Learning.

    Training code
    CGAMLIPYCD
    Spoken Language
    Dutch
    Language Materials
    English
    Dayparts
    4
    Price
    €1.400,00
    excl. VAT No extra costs.

    With an Incompany training you have several advantages:

    - You choose the location
    - You experience the training with your colleagues, so it is always in line with your practice
    - The trainer can tailor explanations, examples and assignments to your organization
    - In consultation exercises can be adapted to organization-specific questions

    Request more information or a quote.

     

    What is Machine Learning in Python

    Large amounts of data can contain many insights. These insights in turn provide added value for business operations. But finding and checking these insights manually takes a lot of time. You would rather do this via a smart automatic process, namely with Machine Learning.
    Machine Learning in Python teaches you how to set up Machine Learning processes in Python, the most widely used programming language in Data Science.
    You will be introduced to terms such as
    CRISP-DM

    • Scikit-learn
    • K-fold Cross-Validation
    • Random Forest
    • K-means Clustering
    • Quantile Regressors
    • Convolutional Neural Network

    We will discuss different algorithms within Machine Learning and how you can apply them in Python. We also discuss how to build a Machine Learning pipeline in Python, how to ensure high data quality and how to detect and prevent model drift.
    During the training, the application of what you learn is central and you are challenged to work with the theory in practical cases. The training is given by people who work in practice with Machine Learning systems in Python and who know what it is about. They want to pass on that practical knowledge.

     
     

    Who should attend Machine Learning in Python

    The Machine Learning in Python training is suitable for:

    • Starters at the start of their career
    • People making a career switch to Data Science
    • People working in IT, especially in departments such as Data Science
    • Business Analysts
    • Information Analysts

    Prerequisites

    Beginning skills and general knowledge of Python. The 'Introduction Python' course addresses these topics and is ideally suited for pre-training.
    With this training you need a laptop.

    Objectives

    After this training, the participant is able to answer the questions:

    • How do you set up a Machine Learning pipeline in Python?
    • What are the advantages and disadvantages of different Machine Learning algorithms?
    • How do you extract insights from large amounts of data?
     
  • Related

    Fields of Expertise
    Data Analytics
     
  • e-CF competences with this course

     

    At Capgemini Academy we believe in transparency and clarity in the training landscape. That is why, in the table below, we show you to which e-CF competence this training or certification contributes. For more information about how to use the e-Competence Framework read more here. If you want to know how you can apply the e-CF within your organization, read more on this page.

    e-Competence Level12345
    A.6.Application Design     
    D.10.Information and Knowledge Management     
    B.1.Application Development     
    E.1.Forecast Development