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Machine Learning in Python (EN)
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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 codeCGAMLIPYCESpoken LanguageEnglishLanguage MaterialsEnglishDayparts4Price€1.500,00excl. VAT No extra costs.Book Machine Learning in Python (EN) 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.
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?
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 codeCGAMLIPYCESpoken LanguageEnglishLanguage MaterialsEnglishDayparts4Price€1.500,00excl. 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?
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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 Level | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| E.1.Forecast Development | |||||
| A.6.Application Design | |||||
| B.1.Application Development | |||||
| D.10.Information and Knowledge Management |