Machine Learning & AI (Python, Ruby on Rail)

Data Science & AI (Artificial Intelligence)

Machine Learning & AI (Python, Ruby on Rail)

The Machine Learning & AI course you mentioned focuses on teaching participants the foundations of machine learning and artificial intelligence, with an emphasis on using Python and Ruby on Rails programming languages. Here are some key points about the course

  • Course Overview: The Machine Learning & AI course provides participants with a comprehensive understanding of machine learning and artificial intelligence concepts. It covers the theoretical foundations as well as practical applications of these technologies.
  • Python Programming: Participants learn Python, one of the most popular programming languages for machine learning and AI. They gain proficiency in Python syntax, data structures, and libraries such as NumPy, Pandas, and Scikit-learn, which are widely used in machine learning projects.
  • Ruby on Rails: The course also introduces Ruby on Rails, a web development framework built using the Ruby programming language. Participants explore how Ruby on Rails can be used to build AI-powered web applications and gain an understanding of its key features and functionality.
  • Machine Learning Fundamentals: Participants learn the fundamental concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. They explore various algorithms and techniques used in machine learning, such as linear regression, logistic regression, decision trees, clustering, and neural networks.
  • AI Fundamentals: The course covers the basics of artificial intelligence, including natural language processing, computer vision, and expert systems. Participants gain insights into how AI algorithms and technologies are used to solve complex problems and enhance decision-making.
  • Data Preprocessing and Feature Engineering: Participants learn how to preprocess and clean raw data to make it suitable for machine learning algorithms. They also explore feature engineering techniques to extract meaningful features from the data, which can improve the performance of AI models.
  • Model Training and Evaluation: The course guides participants through the process of training machine learning models using Python and evaluating their performance. They learn about model selection, hyperparameter tuning, cross-validation, and various evaluation metrics.
  • Real-World Projects: Many Machine Learning & AI courses include hands-on projects where participants apply their knowledge to real-world datasets. This allows them to gain practical experience in developing and deploying machine learning and AI models using Python and Ruby on Rails.

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