Python

Module 1: Basics of Python

• Introduction to Python and its applications

• Installing Python & setting up the environment

• Writing your first Python program

• Variables, Data Types, and Input/Output

• Operators and Expressions

Module 2: Lists and Tuples

• Working with lists and list operations

• Nested lists and list comprehensions

• Tuples and their immutability

• When to use lists vs. tuples

Module 3: Basics of Statistics

• Mean, Median, Mode

• Standard Deviation and Variance

• Introduction to Probability

• Data visualization using matplotlib/seaborn

Module 4: Basics of Jupyter Notebook

• What is Jupyter Notebook?

• Creating and running notebooks

• Markdown cells and code cells

• Importing libraries and visualizing data

Module 5: Introduction to Machine Learning

• What is Machine Learning?

• Types of Machine Learning: Supervised, Unsupervised, Reinforcement

• Overview of ML workflow

• Popular ML tools and libraries (Scikit-learn, Pandas, NumPy)

Module 6: Data Preprocessing

• Loading and inspecting datasets

• Handling missing data

• Encoding categorical variables

• Feature scaling: Normalization & Standardization

• Train-test split

Module 7: Linear Regression

• Understanding the concept of regression

• Implementing simple linear regression using Python

• Evaluating model performance (R², MAE, MSE)

• Visualizing regression results