PYTHON

Python basics Pandas Skitlearn

🐍 Basic Python Course Outline

1. Getting Started

• What is Python and Why Learn It?
• Installing Python on Windows/Mac/Linux
• Writing Your First Python Program

2. Python Fundamentals

• Print Statements
• Variables & Data Types
• User Input
• Basic Operators

3. Control Flow

• If, Elif, Else
• Nested Conditions
• Practical Examples

4. Loops

• For Loops
• While Loops
• Break & Continue
• Loop else
• Nested Loops
• Real-life Examples

6. Built-in Functions

• Introduction to Functions & Built-in Functions
• Type Checking & Conversions
• Mathematical Functionss
• Working with Iterables
• Input & Outputs
• Introspection & Utilitiese
• Common Mistakes with Built-in Functions

6.User defined Functions and special functions

• Defining and Calling Functions
• Arguments & Parameters
• Return values
• Nested Functions
• Lambda Functions
• Map, Filter, Reduce

6. Decorators (Intro)

• Introduction to Decorators
• Basic Syntax of a Decorator
• Decorators with Arguments
• Practical Use Cases
• Building your first decorator
• Common Mistakes & Gotchas
• Practice Exercises

7. Data Structures

• Lists
• Tuples
• Dictionaries
• Sets
• Comprehensions

8. Modules and packages in python

9. File handling and OS interaction in Python

10. Exceptioins and Error handling in Python

• Understanding Errors
• Try and Except Basics
• Finally and Else

11.Bonus:Object-Oriented Programming (OOP) in Python – A Beginner-Friendly Guide

12. Mini Projects

• Simple Calculator
• Number Guessing Game
• Contact Book
• Student Marks Analyzer

🐼 Pandas Course Outline

1. Introduction to Pandas

• What is Pandas and why use it?
• Installing Pandas (pip install pandas)
• Importing Pandas (import pandas as pd)
• Series and DataFrame – Overview

2. Pandas Series

• Creating Series from lists, dicts, and arrays
• Accessing elements in Series
• Series operations and methods
• Indexing, slicing, and filtering

3. Pandas DataFrame Basics

• Creating DataFrames from dicts, lists, CSV, Excel
• Viewing data (head(), tail(), info(), describe())
• Accessing columns and rows
• Filtering data
• Adding, updating, and deleting columns

4. Data Cleaning & Handling Missing Values

• Detecting missing data (isna(), notna())
• Handling missing values (dropna(), fillna())
• Replacing values (replace())
• Renaming columns and indexes

5. Data Selection & Indexing

• Label-based indexing (loc)
• Position-based indexing (iloc)
• Boolean indexing and conditional selection
• Slicing rows and columns

6. Data Aggregation & Grouping

• Basic statistics (mean(), sum(), min(), max())
• GroupBy operations
• Pivot tables
• Aggregations and transformations

7. Merging, Joining, and Concatenation

• Concatenating DataFrames (concat)
• Merging DataFrames (merge)
• Joining on index (join)

8. Data Visualization with Pandas

• Basic plotting (plot()) for Series and DataFrames
• Line plots, bar charts, histograms
• Customizing plots (labels, title, color)

9. Input & Output with Files

• Reading CSV, Excel, and JSON files
• Writing DataFrames to CSV, Excel, and JSON
• Handling large datasets efficiently

10. Advanced Pandas Concepts

• Datetime operations
• Applying functions with apply() and map()
• Working with categorical data
• Multi-indexing (hierarchical indexing)
• Performance tips and vectorized operations

11. Mini Projects

• Analyzing a CSV dataset (e.g., sales data)
• Cleaning messy data and filling missing values
• Calculating statistics like average, sum, max per group
• Plotting graphs from DataFrame

🤖 Scikit-learn Course Outline

1. Introduction

• What is scikit-learn?
• Installation (pip install scikit-learn)
• Core concepts: Estimators, Transformers, Pipelines

2. Working with Data

• Loading built-in datasets (Iris, Digits)
• Loading external datasets with Pandas
• Splitting data (train_test_split)
• Feature scaling & preprocessing (StandardScaler, MinMaxScaler)

3. Supervised Learning – Regression

• Simple & Multiple Linear Regression
• Polynomial Regression
• Regularization: Ridge, Lasso, ElasticNet

4. Supervised Learning – Classification

• k-Nearest Neighbors (k-NN)
• Logistic Regression
• Decision Trees & Random Forests
• Support Vector Machines (SVM)
• Naïve Bayes
• Gradient Boosting (XGBoost, LightGBM)

5. Model Evaluation & Validation

• Accuracy, Precision, Recall, F1-score
• Confusion Matrix
• ROC Curve & AUC
• Cross-validation (cross_val_score)
• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

6. Unsupervised Learning

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

7. Pipelines & Feature Engineering

• Building pipelines (Pipeline, make_pipeline)
• Feature extraction & selection
• Encoding categorical data (OneHotEncoder, LabelEncoder)
• Handling missing values (SimpleImputer)

8. Advanced Topics

• Ensemble Methods (Bagging, Boosting, Stacking)
• Handling imbalanced datasets (SMOTE, class weights)
• Time series forecasting basics
• Custom Transformers & Estimators

9. Real-World Projects

• Predict House Prices (Regression)
• Email Spam Classification
• Customer Churn Prediction
• Image Classification (Digits dataset)
• Market Segmentation (Clustering)