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• What is AI?
o Definition and history of AI
o Types of AI (narrow, general, and superintelligence)
• Machine Learning Basics:
o Supervised, unsupervised, and reinforcement learning
o Feature engineering and model selection
• AI Applications in Everyday Life:
o Examples of AI in various industries (healthcare, finance, entertainment)
• Neural Networks:
o Artificial neurons and perceptrons
o Feedforward neural networks
o Backpropagation and gradient descent
• Deep Learning:
o Convolutional Neural Networks (CNNs) for image recognition
o Recurrent Neural Networks (RNNs) for sequential data
o Generative Adversarial Networks (GANs)
• Hands-on: Building a Simple Neural Network
• Text Processing Techniques:
o Tokenization, stemming, and lemmatization
o Text classification and sentiment analysis
o Text generation and summarization
• Speech Recognition and Synthesis:
o Speech-to-text and text-to-speech conversion
o Voice assistants and chatbots
• Hands-on: Building a Chatbot
• Bias and Fairness in AI:
o Identifying and mitigating bias in AI algorithms
o Ensuring fairness and equity in AI systems
• Privacy and Security:
o Protecting user data and privacy
o Securing AI systems from cyberattacks
• AI for Social Good:
o Using AI to address social and environmental challenges
• Emerging Trends in AI:
o AI and the Internet of Things (IoT)
o AI and robotics
o AI and healthcare
• Career Paths in AI:
o Data scientist
o Machine learning engineer
o AI researcher
o AI product manager
• Building a Successful AI Career:
o Networking and building relationships
o Continuous learning and upskilling