260€
Course on Techniques and Applications of Artificial Intelligence
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200 horas
260€
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In the rapidly evolving landscape of technology, the Course on Techniques and Applications of Artificial Intelligence offers you a gateway to understanding and mastering AI. As industries increasingly rely on AI to drive innovation and efficiency, the demand for skilled professionals in this field is soaring. This course equips you with essential knowledge and insights into the latest AI techniques, empowering you to apply these skills in diverse sectors. By enrolling, you will gain a competitive edge in the job market, developing critical thinking and problem-solving abilities essential for tackling complex challenges. Join us to explore the transformative power of AI and position yourself at the forefront of this exciting technological revolution.
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Para qué te prepara
This course prepares you to harness artificial intelligence in practical scenarios, enhancing your ability to develop and implement AI-driven solutions. You'll gain skills in data analysis, machine learning, and neural networks, enabling you to tackle complex problems with innovative approaches. By the end, you'll be equipped to integrate AI technologies into various sectors, optimising processes and driving strategic decisions with a strong understanding of AI applications.
Objetivos
- To understand the fundamental concepts of artificial intelligence and its applications. - To analyse various AI techniques and evaluate their effectiveness in different scenarios. - To identify ethical considerations and implications of AI in society and business. - To explore machine learning models and their roles in AI-driven solutions. - To assess the impact of AI technologies on current and future industry trends. - To learn about data processing methods and their importance in AI systems. - To gain insights into the development of intelligent systems and their real-world applications.
A quién va dirigido
This course is designed for professionals and graduates in the field who wish to expand or update their knowledge in artificial intelligence. Ideal for those seeking to understand the practical applications and techniques of AI without requiring advanced expertise, providing a solid foundation for career advancement and innovation.
Salidas Profesionales
- AI Engineer in tech companies - Data Scientist in research organisations - Machine Learning Specialist in startups - AI Consultant for business optimisation - Robotics Developer in manufacturing - Natural Language Processing Expert in media - AI Product Manager in software firms - Predictive Modelling Analyst in finance - AI Researcher in academic institutions
Temario
UNIT 1. MACHINE LEARNING
- Machine Learning
- Types of machine learning
- - Supervised
- - Unsupervised
- - Reinforced
- Machine learning algorithms and models
- Evaluation metrics in machine learning
- Regularization and feature selection in machine learning
UNIT 2. ARTIFICIAL NEURAL NETWORKS (ANN)
- Artificial Neural Networks (ANN)
- Structure and architecture
- Activation functions
- Training of the ANNs
- Convolutional Neural Networks (CNN) and their application
- Recurrent Neural Networks (RNN) and their application
- Generative Adversarial Models (GAN) and their application
UNIT 3. NATURAL LANGUAGE PROCESSING (NLP)
- Fundamentals of Natural Language Processing (NLP)
- Language representation in NLP
- - Bag of words
- - Language models
- Feature extraction in NLP
- Sequence-based NLP models
- - LSTM
- - GRU
- - Transformer
- NLP models for specific tasks
- - Text classification
- - Text generation
- - Machine translation
- Applications of NLP
- - Chatbots
- - Sentiment analysis
- - Text summarization
UNIT 4. COMPUTER VISION
- Computer vision
- Image preprocessing and transformation
- - Filters
- - Geometric transformations
- Object detection and recognition
- - Edge detection
- - Feature descriptors
- - Object classifiers
- Image segmentation and classification
- - Semantic segmentation
- - Region-based segmentation
- - Image classification with CNN
- Application of computer vision
- - Facial recognition
- - Autonomous driving
- - Augmented reality
UNIT 5. BIG DATA PROCESSING IN ARTIFICIAL INTELLIGENCE
- Big data in artificial intelligence
- Distributed storage and processing
- - Distributed file systems
- - Hadoop
- - Spark
- Technologies and tools for big data processing
- - MapReduce
- - Pig
- - Hive
- Knowledge extraction from big data
- - Data mining
- - Graph analysis
- Machine learning in big data
- - Distributed learning
- - Mini-batch
- - Stochastic Gradient Descent (SGD)
UNIT 6. OPTIMIZATION AND FINE-TUNING OF AI MODELS
- Model evaluation and performance metrics
- Hyperparameter optimization
- - Grid search
- - Random search
- - Bayesian optimization
- Regularization and overfitting prevention techniques
- Dimensionality reduction techniques
- - Principal Component Analysis (PCA)
- - Feature Selection
- Model tuning and ensemble methods
UNIT 7. REINFORCEMENT LEARNING
- Reinforcement learning
- Agents and environments in reinforcement learning
- Reinforcement learning methods
- - Q-Learning
- - SARSA
- - Actor-Critic
- Exploration and exploitation in reinforcement learning
- Applications of reinforcement learning
- - Games
- - Robotics
UNIT 8. DEPLOYMENT AND PRODUCTION OF AI MODELS
- Data preparation for model deployment
- Design and implementation of AI services
- Monitoring and evaluation of models in production
- Updating and maintenance of AI models
- Scalability and performance in AI model deployment
Titulación
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