•  History 
•  Features 
•  Setting up path 
•  Working with Python 
•  Basic Syntax 
•  Variable and Data Types 
•  Operator

• If-else statements
• Loops (for, while)
• Break, continue, and pass

Functions 

•  Defining a function 
•  Calling a function 
•  Types of functions 
•  Function Arguments 
•  Anonymous functions 
• Global and local variables

Modules 

•  Importing module 
•  Math module 
•  Random module 
•  Packages 
•  Composition

• String
• Lists, tuples, and sets
• Dictionaries
• List comprehensions

• Application of AI
• Deep learning, neutral learning, machine learning
• Introduction to Machine learning
• Supervised learning, (labelled data sets)
• Unsupervised learning, (not labelled)
• Reinforcement learning.

• NumPy and Pandas (for data analysis)
• Matplotlib and Seaborn (for data visualization)
• programs using python data frames

• Handling missing values and outliers
• Data encoding (One-hot, Label encoding)
• Feature scaling (Normalization, Standardization)
• Splitting datasets into training and testing sets

• Regression
  • Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression
• Classification
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Naive Bayes

• Clustering (K-Means, Hierarchical Clustering)
• Dimensionality Reduction (PCA, t-SNE)