Course Overview
- ›Introduction to Python Programming
- ›Installing Python and VS Code
- ›Variables and Data Types
- ›Input and Output
- ›Type Casting
- ›Python Keywords
- ›Writing Your First AI Script
- ›Arithmetic and Logical Operators
- ›Comparison Operators
- ›If-Elif-Else Statements
- ›Nested Conditions
- ›For Loop
- ›While Loop
- ›Break and Continue
- ›Defining Functions
- ›Arguments and Return
- ›Lambda Functions
- ›Variable Scope
- ›Try and Except
- ›Finally Block
- ›Custom Exceptions
- ›Lists and Methods
- ›Tuples
- ›Sets
- ›Dictionaries
- ›List Comprehensions
- ›Dictionary Comprehensions
- ›Nested Data Structures
- ›Classes and Objects
- ›Constructors
- ›Instance vs Class Variables
- ›Inheritance
- ›Polymorphism
- ›Encapsulation
- ›Magic Methods
- ›File Handling
- ›CSV and JSON Handling
- ›Virtual Environments
- ›Recursion
- ›Stacks and Queues
- ›Linked Lists Basics
- ›Time Complexity Introduction
- ›NumPy Arrays
- ›Indexing and Slicing
- ›Broadcasting
- ›Mathematical Operations
- ›Matrix Operations
- ›Random Module
- ›Performance Comparison
- ›Series and DataFrames
- ›Reading CSV and Excel
- ›Data Cleaning
- ›Handling Missing Values
- ›GroupBy Operations
- ›Merging and Joining
- ›Time Series Basics
- ›Matplotlib Basics
- ›Bar and Line Charts
- ›Seaborn Advanced Plots
- ›Plotly Interactive Charts
- ›Dash Dashboards
- ›Folium Maps
- ›Visualization Mini Project
- ›What is Machine Learning?
- ›Supervised vs Unsupervised
- ›Train-Test Split
- ›Overfitting and Underfitting
- ›Bias-Variance Tradeoff
- ›Evaluation Metrics
- ›ML Workflow
- ›Linear Regression
- ›Polynomial Regression
- ›Ridge and Lasso
- ›Gradient Descent
- ›MSE and R2 Score
- ›Regularization
- ›Regression Project
What we'll cover in this course:
- Python Basics for AI
- Operators and Control Flow
- Functions and Exception Handling
- Python Data Structures
- Object Oriented Programming
- Advanced Python for AI
- NumPy for AI
- Pandas for Data Analysis
- Data Visualization with Matplotlib/Seaborn
- Machine Learning Fundamentals
- Regression Algorithms
- Classification Algorithms
- Unsupervised Learning
- Model Optimization
- Neural Network Fundamentals
- TensorFlow and Keras
- Convolutional Neural Networks
- RNN and LSTM
- Computer Vision
- NLP Fundamentals
- Word Embeddings
- Transformers and BERT
- Generative AI Fundamentals
- Advanced Prompt Engineering
- LLM Integration
- Streamlit for GUI
- Building AI Applications
- Freelancing Platforms & Income Models
- Interview Preparation & Case Study Mastery
- Capstone Project
Technologies & Tools
