Introduction to Artificial Intelligence

• What is Artificial Intelligence?
• Types of AI: Narrow AI, General AI, and Superintelligence
• Applications of AI across industries (Healthcare, Finance, Robotics, etc.)

Introduction to Machine Learning

• Supervised vs Unsupervised Learning
• Classification and Regression Algorithms
• Decision Trees, Random Forests, and Support Vector Machines (SVM)
• Hands-on with Python for machine learning

 Deep Learning & Neural Networks

• Introduction to Deep Learning
• Understanding Neural Networks
• Building and training deep neural networks
• Advanced concepts: Convolutional Neural Networks (CNNs) and Recurrent Neural

Natural Language Processing (NLP)

• What is NLP and its real-world applications
• Text Preprocessing and Vectorization
• Working with NLP tools: SpaCy, NLTK, and Hugging Face
• Building chatbots and language models

Reinforcement Learning

• Introduction to Reinforcement Learning
• Key concepts: Agents, Actions, Rewards
• Markov Decision Process (MDP)
• Real-world applications: Game-playing, robotics, etc.

AI Tools & Frameworks

• Introduction to TensorFlow, Keras, and PyTorch
• Hands-on AI model building with TensorFlow
• Implementing deep learning models in Keras
• Building and training models in PyTorch

AI Ethics & Future Trends

• Ethical concerns in AI: Bias, privacy, and transparency
• The impact of AI on society and the workforce
• AI’s role in future technological developments

Final Project & Certification Preparation

• Building a real-world AI application project
• Mock exams and review for certification
• Career guidance: Resume building, interview tips, and job placement support