Ace your next AI job interview. This video covers the most common Artificial Intelligence (AI) interview questions and answers for freshers, including machine learning, deep learning, NLP, computer vision, and more. Learn about common algorithms, data science concepts, and how to explain your AI projects effectively.
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Timestamps:
0:00:01 Introduction
0:00:51 What is the difference between AI, Machine Learning and Deep Learning?
0:03:09 What is Artificial Intelligence? Give the example where AI is used on a daily basis
0:05:12 What are the different types of AI?
0:08:08 How is machine Learning related to Artificial Intelligence?
0:09:30 What is Q-Learning?
0:10:42 What is Deep Learning?
0:12:00 Explain how deep Learning works?
0:13:29 What are Bayesian Networks?
0:14:22 Explain the assessment that is used to test the intelligence of a machine?
0:15:17 What are hyperparameters in Deep Neural networks?
0:16:21 Explain the different algorithms used for hyperparameter optimization?
0:18:14 What is the purpose of Deep Learning frameworks such as a Keras, Tensorflow and PyTorch?
0:19:24 Differentiate between NLP and Text mining
0:20:46 What are the different components of NLP?
0:21:44 Explain the components of expert systems
0:23:03 How is computer vision and AI related
0:23:56 Which is better for image classification? Supervised or unsupervised?
0:24:39 How is game theory and AI related
0:25:06 How can AI be used in detecting fraud
0:27:53 What are intelligent agents
🟢 How do I prepare for an AI interview?
To effectively prepare for an AI interview, thoroughly review fundamental AI/ML concepts, including supervised, unsupervised, and reinforcement learning, common algorithms (e.g., linear regression, decision trees), and data structures. Sharpen your coding skills by practicing on platforms like LeetCode and be prepared to discuss your experience with AI/ML projects, including the challenges you faced and the solutions you implemented.
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Artificial Intelligence Course is a comprehensive skill and application-oriented training program required for developing business models for data analytics. This course will help you learn different aspects of AI like Data Science, Deep Learning, Python Programming, Python Libraries for Artificial Intelligence, etc. During this Artificial Intelligence training, you will get exposure to advanced concepts like TensorFlow, Deep Neural Networks, R Programming, SAS Advanced Analytics, and AWS through real-time projects.
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