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Programming for Machine Learning and Business
Introduction to Python for Machine Learning and Business
Why Python for Business and ML
Python Development Tools (IDEs Jupyter PyCharm Google Colab)
Setting Up Your Python Environment
Writing Your First Python Program
Business Use Cases of Python (Automation Analytics ML)
Python Fundamentals
Variables Data Types and Operators
Control Flow (If Else Loops)
Loops (For While Nested Loops)
Functions and Lambda Expressions
Modules and Packages
Best Practices for Business Oriented Code
Core Data Structures
Lists Operations Slicing and Comprehensions
Dictionaries Key Value Pairs and Methods
Sets Unique Elements and Set Operations
Practice Problems with Dictionaries and Sets
Tuples and Other Built in Types
Choosing the Right Data Structure for Business Problems
Intermediate Python Programming
List Comprehensions and Generator Expressions
File Input Output (CSV Excel JSON XML)
Error Handling and Exceptions
Working with Libraries (NumPy Pandas Matplotlib)
Business Data Formats (Excel PDFs APIs)
Object Oriented Programming OOP
Classes and Objects
Why Object oriented Programming needed
code cell feature
Inheritance and Polymorphism
Static Methods Class Methods and Instance Methods
Decorators Enhancing Functions and Methods
Interactions Between Classes
Exercise Building Classes for ML Pipelines
OOP for Business Applications (Banking HR Retail Examples)
Advanced Python Techniques
Functional Programming (Map Filter Reduce)
Multithreading and Multiprocessing
Working with APIs and Web Scraping
Data Visualization
Matplotlib Plotting Basics
Seaborn for Advanced Visualizations
Interactive Plots with Plotly and Dashboards
Automating Business Reports (Excel PowerPoint PDFs)
Algorithm Implementation in Python
Understanding Algorithms and Data Structures
Arrays Linked Lists and Stacks
Trees Graphs and Hash Tables
Implementing Sorting Algorithms
Advanced Sorting Techniques
LeetCode Easy Problems 50
LeetCode Medium Problems 75
LeetCode Hard Problems 25
Algorithm Optimization Techniques
Time and Space Complexity Analysis
Dynamic Programming and Greedy Algorithms
Business Case Algorithms (Inventory Optimization Pricing Models)
Program Design Principles
Writing Clean and Modular Code
Design Patterns for ML Applications
Documentation Best Practices
Version Control with Git and GitHub
Building End to End Programs for Deployment
Testing and Debugging Business Applications
Database Management with Python
Introduction to Databases (SQL NoSQL and Vector Databases)
SQL with Python (SQLite MySQL PostgreSQL)
NoSQL with Python (MongoDB Firebase)
Data Extraction and Transformation for ML
Database Optimization Techniques
Vector Databases and Semantic Search Systems
Business Data Integration (ERP CRM Finance Systems)
Bash and Linux Scripting for ML and Business Automation
Introduction to Bash and Linux
Automating ML and Data Workflows with Bash
Managing Python Environments in Linux
File System Operations and Scripting
Deploying ML Models on Linux Servers
Scheduling Jobs with Cron for Business Reports
Practical Projects and Exercises
Automating Business Tasks with Python
Data Analysis for Business Insights
Implementing ML Algorithms from Scratch
Deploying a Python Based ML Application
Capstone Project Real Business Case Study
Cloud and Deployment for Business ML
Introduction to Cloud (AWS GCP Azure)
Building Business Dashboards
Containerization with Docker
Scalable Deployment with Kubernetes
Resources and Next Steps
Recommended Python Libraries for ML and Business
LeetCode and Competitive Programming Resources
Communities and Further Learning
Project Ideas for Portfolio Building
Career Paths in Python Data and Business Analytics
Colab
JupyterLite
Repository
Open issue
.ipynb
.pdf
Introduction to Bash and Linux
Introduction to Bash and Linux
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