Artificial Intelligence Professional (AIP)
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how systems interact with data, enabling machines to learn from past experiences and make predictions. The Artificial Intelligence Professional (AIP) certification provides a solid foundation in supervised and unsupervised learning, clustering, decision trees, regression, and the essential mathematical and programming knowledge to apply AI techniques effectively.
Participants will explore the fundamentals of AI, including K-Nearest Neighbors, linear regression, model evaluation, and Python programming with practical guided projects.
ADDRESSED TO
This certification is designed for:
- Anyone interested in expanding their knowledge of Artificial Intelligence and Machine Learning
- Engineers, analysts, and marketing managers
- Data analysts, data scientists, and data administrators
- Professionals and students interested in data mining, classification, and clustering techniques
PURPOSE
The purpose of this course is to:
- Understand the core concepts of Artificial Intelligence and Machine Learning
- Explore supervised and unsupervised learning techniques
- Apply data analysis in decision-making processes
- Gain familiarity with Python programming and its use in AI
- Learn the mathematical foundations relevant to AI algorithms
- Evaluate models effectively using key metrics
- Develop guided AI projects using real-world data
MAIN TOPICS
The course is structured into six main modules:
I. Machine Learning Fundamentals
- Supervised, unsupervised, and reinforcement learning
- Introduction to K-Nearest Neighbors (KNN)
- Euclidean distance and randomness in classification
- Prediction functions and average pricing models
- Model evaluation: RMSE, MAE, quality of predictions
- Handling missing values, normalization, and feature elimination
- Introduction to Scikit-learn
- Guided project: Car price prediction
II. Mathematics for Machine Learning
- Understanding linear and non-linear functions
- Limits and identifying extreme points
III. Linear Algebra for Machine Learning
- Solving linear systems, vectors, and matrix algebra
- Understanding solution sets
IV. Linear Regression in Machine Learning
- Building linear regression models
- Feature selection and transformation
- Gradient descent and least squares
- Guided project: Housing price forecasting
V. Python for Machine Learning
- Logistic regression and classifier evaluation
- Multiclass classification and overfitting
- Clustering fundamentals and K-means algorithm
- Guided project: Stock market prediction
VI. Decision Trees
- How decision trees work and related terminology
- Advantages and limitations
- Pruning and building optimal trees
- Python implementation
- Guided project: Bike rental prediction
Duration:
Duración:
60 min
Number of questions:
40
Minimum passing:
80
Available languages:
English, Spanish, Portuguese
Second chance (free):
SI
Take your exam online.
$150.00
