AIcademy
Machine Learning Fundamentals
Introduction
In this hands-on workshop, you will gain practical experience with the fundamentals of machine learning. You will learn how to apply classic ML techniques — such as regression , classification , decision trees and clustering — and how to train, validate and evaluate models using real datasets.
In addition to building models, you will also work on feature engineering , dealing with incomplete or distorted data, and learn how ML logically fits within broader AI pipelines in addition to, for example, LLMs. This training is ideal for those who want to get a better grip on the underlying principles of AI and really understand and apply machine learning.
Learning objectives
Apply classical ML techniques: linear/logistic regression, k-means clustering, decision trees, random forest
Train, validate and evaluate models using confusion matrix, ROC curve and F1 score
Applying feature engineering: selection, transformation and normalization of input variables
Dealing with data pollution, missing values and bias in datasets
Integrating ML into broader AI workflows: from model development to production
Approach and working methods
During this workshop, you will work hands-on with realistic datasets from different domains (e.g. healthcare, finance or customer data). You will build complete ML workflows from preprocessing to evaluation and discuss the impact of choices such as model selection and hyperparameter tuning.
Tools such as scikit-learn , Pandas , NumPy and Jupyter Notebooks are used.
If you are interested, we will tailor the training to specific domains or existing data projects of your organization.
For whom
This training is intended for data specialists, engineers, analysts or developers who want to do more with AI than just LLMs, and want to delve into the classical machine learning approach as a foundation for modern AI applications.
Interested in this training?
Feel free to contact us. We are happy to think along with you about a training that matches the knowledge and goals of your team or organization.
Machine Learning Fundamentals
Introduction
In this hands-on workshop, you will gain practical experience with the fundamentals of machine learning. You will learn how to apply classic ML techniques — such as regression , classification , decision trees and clustering — and how to train, validate and evaluate models using real datasets.
In addition to building models, you will also work on feature engineering , dealing with incomplete or distorted data, and learn how ML logically fits within broader AI pipelines in addition to, for example, LLMs. This training is ideal for those who want to get a better grip on the underlying principles of AI and really understand and apply machine learning.
Learning objectives
Apply classical ML techniques: linear/logistic regression, k-means clustering, decision trees, random forest
Train, validate and evaluate models using confusion matrix, ROC curve and F1 score
Applying feature engineering: selection, transformation and normalization of input variables
Dealing with data pollution, missing values and bias in datasets
Integrating ML into broader AI workflows: from model development to production
Approach and working methods
During this workshop, you will work hands-on with realistic datasets from different domains (e.g. healthcare, finance or customer data). You will build complete ML workflows from preprocessing to evaluation and discuss the impact of choices such as model selection and hyperparameter tuning.
Tools such as scikit-learn , Pandas , NumPy and Jupyter Notebooks are used.
If you are interested, we will tailor the training to specific domains or existing data projects of your organization.
For whom
This training is intended for data specialists, engineers, analysts or developers who want to do more with AI than just LLMs, and want to delve into the classical machine learning approach as a foundation for modern AI applications.
Interested in this training?
Feel free to contact us. We are happy to think along with you about a training that matches the knowledge and goals of your team or organization.
Description:
Gain a practical grasp of classic machine learning techniques such as classification, regression, and clustering, including model selection and evaluation.
Learning objectives:
Applying classical ML techniques: regression, decision trees, clustering.
Train, validate and evaluate models (confusion matrix, F1 score).
Feature engineering and dealing with bias or data pollution.
Integrating ML into AI workflows and pipelines.
For whom: Data specialists, engineers and professionals with technical knowledge who want to use AI more broadly than just LLMs.
Machine Learning Fundamentals
Introduction
In this hands-on workshop, you will gain practical experience with the fundamentals of machine learning. You will learn how to apply classic ML techniques — such as regression , classification , decision trees and clustering — and how to train, validate and evaluate models using real datasets.
In addition to building models, you will also work on feature engineering , dealing with incomplete or distorted data, and learn how ML logically fits within broader AI pipelines in addition to, for example, LLMs. This training is ideal for those who want to get a better grip on the underlying principles of AI and really understand and apply machine learning.
Learning objectives
Apply classical ML techniques: linear/logistic regression, k-means clustering, decision trees, random forest
Train, validate and evaluate models using confusion matrix, ROC curve and F1 score
Applying feature engineering: selection, transformation and normalization of input variables
Dealing with data pollution, missing values and bias in datasets
Integrating ML into broader AI workflows: from model development to production
Approach and working methods
During this workshop, you will work hands-on with realistic datasets from different domains (e.g. healthcare, finance or customer data). You will build complete ML workflows from preprocessing to evaluation and discuss the impact of choices such as model selection and hyperparameter tuning.
Tools such as scikit-learn , Pandas , NumPy and Jupyter Notebooks are used.
If you are interested, we will tailor the training to specific domains or existing data projects of your organization.
For whom
This training is intended for data specialists, engineers, analysts or developers who want to do more with AI than just LLMs, and want to delve into the classical machine learning approach as a foundation for modern AI applications.
Interested in this training?
Feel free to contact us. We are happy to think along with you about a training that matches the knowledge and goals of your team or organization.
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