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Machine Learning in Python (NL)
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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 codeCGAMLIPYCDSpoken LanguageDutchLanguage MaterialsEnglishDayparts4Price€1.600,00excl. VAT No extra costs.Book Machine Learning in Python (NL) 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.
Machine Learning in Python (NL)
10
10.0
0
2 reviewsWhat is Machine Learning in Python
Machine Learning in Python is a comprehensive training that introduces you to the process of extracting insights from large amounts of data using Python, the most widely used programming language in Data Science. You will learn about various concepts such as CRISP-DM, Scikit-learn, K-fold Cross-Validation, Random Forest, K-means Clustering, Quantile Regressors, and Convolutional Neural Network. The training will guide you on how to set up a Machine Learning pipeline in Python, improve data quality, and detect and prevent model drift. The training emphasizes the application of learned concepts through practical cases, facilitated by trainers who have real-world experience with Machine Learning systems in Python.
Training in Machine Learning in Python is a valuable investment. Not only will you learn theoretical concepts, but you will also gain practical insights and best practices from our trainers who are experts in the field. Their expertise adds a practical dimension to the theoretical concepts, providing real-world insights and best practices.
Who should attend Machine Learning in Python
- Data Analysts: Enhance your data analysis skills by learning to extract insights from large datasets.
- Data Scientists: Learn to build efficient Machine Learning pipelines in Python.
- Data Engineers and Software Engineers: Expand your skill set by learning the most widely used programming language in Data Science.
Prerequisites
Beginning skills and general knowledge of Python. The 'Introduction Python' training addresses these topics and is ideally suited for pre-training.
During this training you need a laptop on which you can install software: Python.Objectives
At the end of the training, you will be able to:
- Set up a Machine Learning pipeline in Python.
- Understand the advantages and disadvantages of different Machine Learning algorithms.
- Extract insights from large amounts of data.
This training is designed to provide you with the most relevant and up-to-date knowledge in the field of Machine Learning using Python.
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 codeCGAMLIPYCDSpoken LanguageDutchLanguage MaterialsEnglishDayparts4Price€1.600,00excl. 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.Machine Learning in Python (NL)
10
10.0
0
2 reviewsWhat is Machine Learning in Python
Machine Learning in Python is a comprehensive training that introduces you to the process of extracting insights from large amounts of data using Python, the most widely used programming language in Data Science. You will learn about various concepts such as CRISP-DM, Scikit-learn, K-fold Cross-Validation, Random Forest, K-means Clustering, Quantile Regressors, and Convolutional Neural Network. The training will guide you on how to set up a Machine Learning pipeline in Python, improve data quality, and detect and prevent model drift. The training emphasizes the application of learned concepts through practical cases, facilitated by trainers who have real-world experience with Machine Learning systems in Python.
Training in Machine Learning in Python is a valuable investment. Not only will you learn theoretical concepts, but you will also gain practical insights and best practices from our trainers who are experts in the field. Their expertise adds a practical dimension to the theoretical concepts, providing real-world insights and best practices.
Who should attend Machine Learning in Python
- Data Analysts: Enhance your data analysis skills by learning to extract insights from large datasets.
- Data Scientists: Learn to build efficient Machine Learning pipelines in Python.
- Data Engineers and Software Engineers: Expand your skill set by learning the most widely used programming language in Data Science.
Prerequisites
Beginning skills and general knowledge of Python. The 'Introduction Python' training addresses these topics and is ideally suited for pre-training.
During this training you need a laptop on which you can install software: Python.Objectives
At the end of the training, you will be able to:
- Set up a Machine Learning pipeline in Python.
- Understand the advantages and disadvantages of different Machine Learning algorithms.
- Extract insights from large amounts of data.
This training is designed to provide you with the most relevant and up-to-date knowledge in the field of Machine Learning using Python.
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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 |
|---|---|---|---|---|---|
| A.6.Application Design | |||||
| D.10.Information and Knowledge Management | |||||
| B.1.Application Development | |||||
| E.1.Forecast Development |