• What is Artificial Intelligence?
• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Introduction to the course curriculum and learning objectives

• Installing Python and Anaconda
• Introduction to Python syntax, data types, and variables
• Basic arithmetic operations in Python

• Conditional statements (if-else)
• Looping statements (for and while loops)
• Nested loops and loop control statements

• Lists: indexing, slicing, and manipulation
• Tuples: creating, accessing, and modifying
• Dictionaries: key-value pairs and operations
• Sets: creating, adding, and removing elements

• NumPy arrays: creation, indexing, and slicing
• Mathematical operations with NumPy arrays
• Array manipulation and reshaping

• Pandas data structures: Series and DataFrame
• Reading data from different sources
• Data indexing, selection, and filtering in Pandas

• Handling missing values
• Removing duplicates
• Dealing with outliers
• Feature scaling and normalization

• What is Machine Learning?
• Steps in the ML process: Data preprocessing, model training, evaluation, and prediction
• Supervised learning, unsupervised learning, and reinforcement learning

• Introduction to Linear Regression
• Simple Linear Regression: concepts, implementation, and evaluation
• Multiple Linear Regression: concepts, implementation, and evaluation

• Introduction to Logistic Regression
• Logistic Regression for binary classification: concepts, implementation, and evaluation
• Multinomial Logistic Regression: concepts, implementation, and evaluation

• Introduction to Decision Trees
• Decision Tree algorithm: concepts, implementation, and evaluation
• Ensemble methods: Introduction to Random Forests

• Introduction to Naive Bayes algorithm
• Types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli
• Implementing Naive Bayes algorithm for classification

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