CERTIFIED ARTIFICIAL INTELLIGENCE DEVELOPER | CAID

  Week 1-2: Introduction to Al and ML

•	Overview of Artificial Intelligence and Machine Learning
•	Historical Perspective and Evolution of Al
•	Ethical and Social Implications of Al
•	Introduction to Python Programming for AI & ML

  Week 3-5: Python Fundamentals for AI & ML

• Basics of Python Programming Language
• Data Structures and Manipulation in Python
• Introduction to Libraries such as NumPy, Pandas and Matplotlib
• Hands-on Exercises using Jupyter Notebook

  Week 6-9: Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Model Evaluation and Validation Techniques
• Practical Implementation using Scikit-learn and TensorFlow/Keras

  Week 10-12: Unsupervised Learning

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection Techniques
• Implementation with Scikit-learn and TensorFlow/Keras

  Week 13-15: Deep Learning and Neural Networks

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning and Fine-tuning
• Hands-on Projects using TensorFlow/Keras and PyTorch

  Week 16-18: Natural Language Processing (NLP)

• Text Preprocessing Techniques
• Sentiment Analysis
• Named Entity Recognition (NER)
Sequence-to-Sequence Models for Machine Translation
• Implementation with NLTK, SpaCy, and TensorFlow/Keras

  Week 19-21: Reinforcement Learning

• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradient Methods
• Applications in Robotics and Gaming
• Practical Implementation using OpenAl Gym and TensorFlow

  Week 22-25: Real-World Applications of AI & ML

•	Healthcare Informatics
•	Financial Forecasting and Trading
•	Autonomous Vehicles and Robotics
•	Recommender Systems
•	Image and Speech Recognition

  Week 26-28: Capstone & Live Project

• Apply knowledge and skills acquired throughout the course to complete a comprehensive capstone project.
• Work on a real-world problem statement using Al and ML techniques. Utilize Python
• Utilize Python programming and industry-standard tools and software.
• Present findings and solutions to peers and instructors.