Requirements

• Understanding of Machine Learning Concepts
• Basic Knowledge Deep Learning and NLP
• Interest in Generative AI

Description

This comprehensive course is designed to prepare you thoroughly for generative AI interviews by covering essential topics across machine learning, deep learning, natural language processing (NLP), generative models, ethics, and practical skills/tools. Each section is meticulously crafted to provide in-depth knowledge and practical insights, ensuring you master every aspect of generative AI technology.

Course Topics Covered:

1. Fundamentals of Machine Learning for Generative AI

• Delve into foundational concepts such as supervised learning (classification, regression), unsupervised learning (clustering, PCA), reinforcement learning (Q-learning, policy gradients), and essential evaluation metrics like accuracy, precision, and F1 score.

2. Deep Learning for Generative AI

• Explore neural networks architecture, activation functions, convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) including LSTM and GRUs for sequence modeling, optimization techniques such as gradient descent and Adam, and regularization techniques like dropout and batch normalization.

3. Natural Language Processing (NLP) for Generative AI

• Master text processing techniques such as tokenization, stemming, lemmatization, and stop words removal. Dive into language models like N-grams, Markov chains, word2vec, and GloVe embeddings. Understand transformers architecture, attention mechanisms, and their applications in models like BERT and GPT for tasks such as sentiment analysis, machine translation, and text summarization.

4. Generative Models

• Gain proficiency in variational autoencoders (VAEs) and their applications in generative tasks. Learn the principles, architecture, and applications of generative adversarial networks (GANs). Explore diffusion models, their concepts, applications, and comparative analysis with other generative models.

5. Ethics and Fairness in Generative AI

• Address critical issues such as bias and fairness in AI, strategies for measuring and mitigating bias, principles of privacy including differential privacy and federated learning, and the ethical implications of generative AI on society including considerations for misuse potential.

6. Practical Skills and Tools for Generative AI

• Equip yourself with essential tools and frameworks like TensorFlow, PyTorch, and Hugging Face Transformers. Learn best practices for handling data pipelines and managing large datasets. Master the art of model deployment including API development, containerization using Docker, and effective model serving techniques.

Conclusion:

By the end of this course, you will have not only answered over 500+ crucial interview questions but also gained a deep understanding and practical proficiency in generative AI. Whether you're aiming to crack interviews or deepen your expertise in the field, this course provides the comprehensive knowledge and confidence needed to excel in generative AI technology.

Who this course is for:

• Anyone Preparing for Generative AI Interviews
• Students and Graduates: Seeking to enhance their knowledge and prepare for interviews in the field of AI
• particularly generative AI.