AI FUNDAMENTALS: Introduction to AI and machine learning, covering the basics of AI and the different types of machine learning. This includes understanding supervised, unsupervised, and reinforcement learning techniques and their real-world applications.

PYTHON FOR AI: Dive into Python for AI development, setting up your Python development environment and exploring essential libraries such as NumPy for numerical operations, Pandas for data manipulation, Scikit-learn for machine learning algorithms, and TensorFlow for deep learning. You will learn to write clean, efficient, and maintainable code in Python.

DATA PREPARATION AND EXPLORATION : Learn comprehensive techniques for data cleaning, normalization, and transformation to prepare datasets for analysis. You will conduct exploratory data analysis (EDA) to uncover patterns, correlations, and insights within the data and use visualization tools to present your findings effectively.

BUILDING AND TRAINING AI MODELS:  Implement various machine learning algorithms, including linear regression, logistic regression, decision trees, Random Forests, and Support Vector Machines (SVM). You will understand the theory and mathematics behind each algorithm and follow a step-by-step guide to training, validating, and testing your models.

MODEL EVALUATION AND  OPTIMIZATION:  Learn how to evaluate model performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC. You will explore hyperparameter tuning techniques, such as Grid Search and Random Search, to enhance model performance and address issues like overfitting and underfitting

MODEL DEPLOYMENT AND  MAINTENANCE:  Learn  to prepare your AI model for deployment in production environments. This includes utilizing platforms like Flask and Django to create APIs for model deployment, best practices for monitoring deployed models, managing version control, and ensuring continuous improvement and adaptation to new data.