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| Course Fee | 1,700 |
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Total Fees |
1,700 (USD) |
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Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
Koenig Learning Stack
Free Pre-requisite TrainingJoin a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.
Assessments (Qubits)Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.
Post Training ReportsReceive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.
Class RecordingsGet access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.
Free Lab ExtensionsExtend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.
Free Revision Classes Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
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.
