Python & Machine Learning Online Course

Course Overview

• Course Duration:4 Months (Including Lab Work, Internship, and real-world assignment).
• Modes of Training: Online Classes/Offline Training (at selected centers).

Python & ML Online Course

• Learn Python programming basics, data structures, and OOP concepts.
• Master data preprocessing, cleaning, and visualization using Pandas and Matplotlib.
• Understand and implement Machine Learning algorithms from scratch.
• Train and evaluate predictive models using Scikit-Learn and TensorFlow.
• Work on real-world projects including classification, regression, and clustering.
• Prepare for professional Python & Machine Learning credential preparation Exams and career roles.

Overview of Python & Machine Learning Online Course

The Python & Machine Learning Online Course is designed for beginners and professionals who want to master data analytics and AI-based problem-solving. This course blends programming, statistics, and real-world model development in a hands-on and easy-to-understand format, providing the specialized technical expertise needed to advance your career in the field of artificial intelligence.

Through hands-on labs and guided projects, you'll learn how to handle data, train algorithms, and deploy predictive models. By the end of the course, you'll be ready to earn a credential preparation and build a strong foundation for Data Science or AI careers.

1. Python Essentials

• Python Setup and Installation.
• Basic Commands, Data Types, Variables.
• Python Constructs and Operators.
• Control Statements and Loops.

2. Object-Oriented Programming (OOP)

• Inheritance, Encapsulation, Polymorphism.
• Database Connectivity using Python.

3. Database Integration

• Python-MySQL Connector.
• Database CRUD Operations.

4. Python Libraries for Data Science

• NumPy for Mathematical Computing.
• SciPy for Scientific Computing.
• Matplotlib for Data Visualization.
• Pandas for Data Analysis.
• BeautifulSoup & lxml Libraries.
• Scraping, Parsing, Navigating HTML.
• Extracting and Printing Data.

6. Machine Learning Modules

• Supervised & Unsupervised Learning.
• Regression, Classification, Clustering.
• Model Evaluation & Tuning.
• ML with Scikit-learn & TensorFlow.
• Multithreading & Race Conditions.
• Packages, Functions & Decorators.