This course is designed for professionals seeking to apply Artificial Intelligence (AI) and Machine Learning (ML) tools in real-world business environments, using Python, modern frameworks, and automation methodologies focused on productivity and return on investment. The training covers everything from theoretical foundations to the deployment of intelligent solutions, with a practical, project-based approach.
Upon completing the course, the participant will be able to:
- Apply AI and ML concepts in business environments
- Use Python and data science libraries for data manipulation and analysis
- Train supervised and unsupervised models
- Apply Natural Language Processing (NLP) and generative artificial intelligence
- Deploy models using APIs and containers
- Integrate intelligent agents (LLMs) into real-world processes
- Automate business workflows with a focus on return on investment (ROI)
- Develop and present a real-world intelligent automation project
To participate in this training, attendees must meet the following requirements:
- Solid foundation in programming logic and data structures
- Practical knowledge of Python, including functions, control structures, lists, and file handling
- Experience with frameworks such as Django or Flask
- Basic understanding of linear algebra and statistics
- Familiarity with Jupyter Notebook
- Ability to work with data formats such as CSV, Excel, and JSON
- Desirable: Experience in projects involving automation, data analysis, or visualization
| AI and Machine Learning – Business Implementation Track | Applies |
|---|---|
| AI and Machine Learning – Business Implementation Track | 60 hours |
Learning Methodology
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor (live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
The methodology seeks that the student does not memorize, but rather understands the concepts and how they are applied in a work environment.
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Pre-enrollment
You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.
Pre-enroll nowInfinity Payments
Make your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
Description
This course is designed for professionals seeking to apply Artificial Intelligence (AI) and Machine Learning (ML) tools in real-world business environments, using Python, modern frameworks, and automation methodologies focused on productivity and return on investment. The training covers everything from theoretical foundations to the deployment of intelligent solutions, with a practical, project-based approach.
Objectives
Upon completing the course, the participant will be able to:
- Apply AI and ML concepts in business environments
- Use Python and data science libraries for data manipulation and analysis
- Train supervised and unsupervised models
- Apply Natural Language Processing (NLP) and generative artificial intelligence
- Deploy models using APIs and containers
- Integrate intelligent agents (LLMs) into real-world processes
- Automate business workflows with a focus on return on investment (ROI)
- Develop and present a real-world intelligent automation project
To participate in this training, attendees must meet the following requirements:
- Solid foundation in programming logic and data structures
- Practical knowledge of Python, including functions, control structures, lists, and file handling
- Experience with frameworks such as Django or Flask
- Basic understanding of linear algebra and statistics
- Familiarity with Jupyter Notebook
- Ability to work with data formats such as CSV, Excel, and JSON
- Desirable: Experience in projects involving automation, data analysis, or visualization
offers
| AI and Machine Learning – Business Implementation Track | Applies |
|---|---|
| AI and Machine Learning – Business Implementation Track | 60 hours |
Learning Methodology
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor(live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
La metodología persigue que el estudiante "does not memorize", but rather "understands" the concepts and how they are applied in a work environment."
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Infinity Payments
Make your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
Course Modules
Module 1: Fundamentals of AI, ML, and Applied Mathematics
- Introduction to AI, Machine Learning, and Deep Learning
- Weak, Strong, and General AI
- Applications in Business and Industry
- Basic Linear Algebra for ML: Vectors, Matrices, Operations
- Applied Probability and Statistics: Distributions, Mean, Variance, Bayes, Decision Trees
- Ethics, Biases, and Social Responsibility of AI (Cross-Cutting Approach)
Module 2: Python for Data Science and Dataset Manipulation
- Jupyter Notebook as a working environment
- Key libraries: NumPy, Pandas, Matplotlib, Seaborn
- Cleaning, transformation, and analysis of real data
- Handling null values, encoding, scaling
- Visualization and interpretation of variables and correlations
Module 3: Supervised Learning and Model Evaluation
- Linear and logistic regression
- Decision trees, Random Forest, Gradient Boosting
- SVM: fundamentals and application
- Model evaluation: accuracy, recall, precision, F1-score, ROC-AUC
- Cross-validation, overfitting, and model tuning
Module 4: Unsupervised Learning and Advanced Analysis
- Clustering: K-means, DBSCAN, hierarchical
- Dimensionality reduction: PCA, t-SNE
- Outlier analysis and segmentation
- Advanced visualization of groups and patterns
Module 5: Natural Language Processing (NLP)
- Text cleaning: tokenization, stemming, lemmatization
- Representations: Bag of Words, TF-IDF, Word2Vec
- Text classification, sentiment analysis
- Introduction to transformer models and embeddings
- Real-world applications: customer service, review analysis
Module 6: Deep Learning for Intelligent Automation
- Artificial Neural Networks with Keras and TensorFlow
- Convolutional Neural Networks (CNN) for Images
- RNN and LSTM for Time Series and Text
- Regularization Techniques: Dropout, Early Stopping
- Hyperparameter Tuning
Module 7: MLOps and Model Deployment
- RESTful APIs with Flask/FastAPI for serving models
- Introduction to Docker: containers for AI
- MLFlow for model and dataset versioning
- Automation of training workflows and basic monitoring
Module 8: AI Agents and LLMs for Businesses
- Introduction to LLMs (GPT, Claude, Mistral, etc.)
- Creating Intelligent Assistants with ChatGPT and Hugging Face
- Integration with Internal Systems: CRMs, ERPs, Processes
- Practical Applications: Content Generation, Automated Support, Internal Assistance
Module 9: Business Automation with AI
- Detection of automatable processes
- Automation of decisions and predictions
- Real cases: marketing, logistics, sales, HR, and sustainability
- Design of solutions focusing on ROI, scalability, and innovation
Module 10: Final Project on Intelligent Automation
- Development of a real solution applying everything learned
- Presentation of the model, generated impact, and expected return
- Technical documentation of the project: operation, application, addressed need
- Upload of the project to a version-controlled repository
- Peer and instructor evaluation