Course Overview
- ›Installing Python on Windows/Mac
- ›Installing VS Code
- ›Installing Python Extension in VS Code
- ›Setting up Virtual Environment (venv)
- ›Installing Required Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
- ›Understanding Integrated Terminal
- ›Running Python Scripts in VS Code
- ›Managing Project Folder Structure
- ›Introduction to Google Colab
- ›Creating and Managing Notebooks
- ›Using Code Cells and Markdown Cells
- ›Installing Libraries in Colab
- ›Uploading and Accessing Datasets
- ›Using Google Drive with Colab
- ›Understanding AI in Google Colab
- ›Sharing and Exporting Notebooks
- ›What is Data Analytics?
- ›Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- ›Data Types, Data Sources and Formats
- ›Data Cleaning and Preprocessing Concepts
- ›Analytics vs Data Science vs AI
- ›Industry Use Cases
- ›Python Installation and Environment Setup
- ›Variables and Data Types
- ›Numeric Data Types (int, float)
- ›String Data Type and String Operations
- ›String Methods (split, replace, join, strip, etc.)
- ›Type Casting in Python
- ›Conditional Statements (if, elif, else)
- ›Nested If Conditions
- ›For Loop
- ›While Loop
- ›Loop Control Statements (break, continue, pass)
- ›Functions (User-defined Functions)
- ›Lists and List Operations
- ›Tuples and Tuple Operations
- ›Dictionaries and Dictionary Methods
- ›Sets and Set Operations
- ›Lambda Functions
- ›Basic Object-Oriented Programming
- ›NumPy Arrays and Operations
- ›Indexing and Slicing
- ›Mathematical and Statistical Functions
- ›Multi-Dimensional Arrays
- ›Reading and Writing CSV Files
- ›Working with Excel Files
- ›Series and DataFrame
- ›Data Cleaning and Handling Missing Values
- ›Filtering and Sorting Data
- ›GroupBy Operations
- ›Merging and Joining DataFrames
- ›Line Charts
- ›Bar Charts
- ›Histograms
- ›Scatter Plots
- ›Box Plots
- ›Customizing Visualizations
- ›Heatmaps
- ›Pairplots
- ›Correlation Visualization
- ›What is Machine Learning?
- ›Types of Machine Learning
- ›Supervised vs Unsupervised Learning
- ›ML Workflow
- ›Introduction to Scikit-Learn Library
- ›Understanding Dataset Structure
- ›Train-Test Split
- ›Building Models
- ›Model Evaluation Techniques
- ›Confusion Matrix
- ›Overfitting and Underfitting
- ›Saving Models
- ›Regression vs Classification
- ›Feature Selection Basics
- ›Model Training Process
- ›Model Evaluation Overview
- ›Linear Regression
- ›Polynomial Regression
- ›Random Forest Regressor
- ›MAE, MSE, RMSE
- ›R2 Score
What we'll cover in this course:
- Setting up VS Code Environment
- Understanding Google Colab Notebook
- Introduction to Data Analytics
- Python Revision (for Data Science)
- NumPy for Data Analysis
- Pandas for Data Preprocessing
- Data Visualization with Matplotlib/Seaborn
- Machine Learning Fundamentals
- Machine Learning with Scikit-Learn
- Supervised Learning
- Regression Algorithms
- Classification Algorithms
- Unsupervised Learning
- Introduction to Artificial Intelligence
- LLM Integration
- Project
Technologies & Tools
