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We're here to help you find itThe Artificial Intelligence (AI) and Machine Learning (ML) course is a comprehensive journey through the cutting-edge technologies shaping the future of computing. Starting with Module 1, learners will delve into Convolutional Neural Networks (CNN), understanding the fundamental layers such as Relu layer, pooling, flattening, and full connections. They'll gain hands-on experience in building and evaluating CNN models for Image classification.
In Module 2, the focus shifts to Recurrent Neural Networks (RNN), tackling the Vanishing Gradient Problem and exploring Long Short-Term Memory (LSTM) networks. Participants will learn to construct RNN models and apply them to real-world forecasting scenarios.
Module 3 introduces Restricted Boltzmann Machines (RBM), an energy-based model, and dives into contrastive divergence. Learners will build their own Boltzmann machines, explore machine evaluation techniques, and study practical applications through case studies.
This course will empower learners with the skills to develop sophisticated AI models, offering a competitive edge in the rapidly evolving field of artificial intelligence and machine learning.
Successfully delivered 5 sessions for over 6 professionals
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| Course Fee | 1,200 |
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Total Fees |
1,200 (USD) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Certainly! For students interested in enrolling in the Artificial Intelligence (AI) and Machine Learning (ML) course focusing on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Restricted Boltzmann Machines (RBM), the following minimum prerequisites are recommended to ensure a successful learning experience:
Basic understanding of machine learning concepts: Familiarity with the fundamental principles of machine learning, such as supervised and unsupervised learning, classification, regression, and common algorithms.
Fundamentals of deep learning: Awareness of deep learning concepts and neural networks, including what they are and how they function.
Mathematical knowledge: A good grasp of linear algebra, calculus, and probability. Understanding matrix operations, derivatives, and probability distributions will be essential for grasping the mathematical underpinnings of AI and ML models.
Programming skills: Proficiency in a high-level programming language, preferably Python, as it is commonly used for AI and ML development. Knowledge of Python libraries such as NumPy, Pandas, and Matplotlib will be beneficial.
Basic statistics: Understanding of basic statistical concepts, such as mean, median, variance, and standard deviation, is important for interpreting model performance and data analysis.
Software installation: Ability to install and run necessary software and tools on your computer, including development environments and ML libraries like TensorFlow or Keras.
These prerequisites are designed to ensure that you have the foundational knowledge and skills needed to fully engage with the course material and gain the most from your AI and ML training. While the course is comprehensive, a foundation in the above areas will greatly enhance your learning experience and facilitate a smoother progression through the more advanced topics covered in the course.
Koenig Solutions' AI and ML course offers in-depth training in advanced neural networks, tailored for tech professionals seeking specialized knowledge.
Target audience for the Artificial Intelligence (AI) and Machine Learning (ML) course includes:
This AI and ML course equips students with an in-depth understanding of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Restricted Boltzmann Machines (RBM), focusing on model construction, troubleshooting, and application.
