Description:

This course provides an advanced understanding of machine learning algorithms and techniques. It focuses on exploring advanced concepts and methodologies used in machine learning with practical applications. Learners will gain in-depth knowledge of advanced algorithms such as support vector machines, artificial neural network, deep learning, reinforcement learning, recommender systems, and time series analysis etc. They will also learn about model optimization, hyper parameter tuning, and ensemble methods. By the end of this course, learners will have the skills to tackle complex machine learning problems and develop innovative solutions in various domains.

Key Highlights:

• Explore advanced machine learning algorithms
• Gain in-depth knowledge of support vector machines, artificial neural network, deep learning, reinforcement learning, and recommender systems and time series analysis
• Learn hyperparameter tuning, model optimization 
• Discover ensemble methods for improved performance
• Apply machine learning concepts to real-world problems

What you will learn:

• Learning Outcome 1
  Understand and implement advanced machine learning algorithms such as support vector machines, artificial neural networks, deep learning, reinforcement learning, time series analysis etc.
• Learning Outcome 2
  Apply model optimization techniques to improve the performance and accuracy of machine learning models
• Learning Outcome 3
  Gain knowledge of ensemble methods and their applications to enhance the predictive power of machine learning models
• Learning Outcome 4
  Utilize machine learning concepts to solve real-world problems across various domains