AI/ML Engineer
Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, enabling them to perform tasks that typically require human-like understanding. Machine Learning, a subset of AI, focuses on developing algorithms that allow computers to learn from and make predictions based on data without being explicitly programmed. Through the use of algorithms and data, […]
Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, enabling them to perform tasks that typically require human-like understanding. Machine Learning, a subset of AI, focuses on developing algorithms that allow computers to learn from and make predictions based on data without being explicitly programmed. Through the use of algorithms and data, machine learning enables systems to improve their performance over time, making it a crucial component of modern AI applications across various fields such as healthcare, finance, and autonomous vehicles.
Lesson1
Artificial Intelligence (AI) simulates human intelligence in machines. Machine Learning, a subset of AI, enables computers to learn from data and make predictions without explicit programming.
Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and techniques enabling computers to learn from data and make predictions or decisions without being explicitly programmed. At its core, machine learning involves the creation of models that can automatically learn and improve from experience.
Lesson2
Essential mathematical principles for understanding AI and ML algorithms.
Overview of programming languages commonly used in AI and ML development.
Methods for preparing and cleaning data before applying machine learning algorithms.
Lesson3
Introduction to deep learning algorithms and architectures.
Study of techniques for analyzing and understanding human language.
Lesson4
Introduction to methods for interpreting and analyzing visual data.
Study of learning algorithms based on interaction with an environment.
Lesson5
Techniques for assessing and improving the performance of machine learning models.
Methods for selecting relevant features and reducing the dimensionality of data.
Study of techniques that combine multiple models to improve predictive performance.
Lesson6
Process of deploying trained models into production environments.
Techniques for understanding and interpreting the decisions made by AI models.
Lesson7
Overview of popular frameworks and libraries used for developing AI and ML applications.
Application of learned concepts to real-world projects using publicly available datasets.
Lesson8
Real-World AI/ML Application Development: Leveraging Learned Concepts to Solve a Practical Problem
